For the week’s topics of Conduct Problems and ADHD, analyze the primary arguments presented in either one of additional articles posted on Canvas OR a relevant empirical, peer-reviewed article of your choosing.
Discuss how the author’s perspective contributes to the broader academic conversation on these subjects. Reflect on the strengths and limitations of the author’s arguments, providing specific examples from the text. Include your critical evaluation of the evidence presented and how it supports or contradicts other sources you have encountered or your current knowledge of the study of abnormal child psychology. Ensure you properly cite (APA formatting, 7th edition) the additional articles from Canvas in your discussion.
Feel free to let me know if you need any more assistance.
Vol.:(0123456789)1 3
Research on Child and Adolescent Psychopathology
https://doi.org/10.1007/s10802-020-00713-9
Inhibitory Control Deficits in Children with Oppositional Defiant
Disorder and Conduct Disorder Compared to Attention Deficit/
Hyperactivity Disorder: A Systematic Review and Meta‑analysis
Mikaela D. Bonham1 · Dianne C. Shanley1 · Allison M. Waters1 · Olivia M. Elvin1
Accepted: 24 September 2020
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Inhibitory control decits are known to be characteristic of Oppositional Deant Disorder (ODD), Conduct Disorder (CD),
and Attention-Decit/Hyperactivity Disorder (ADHD); but it is unclear whether children with ODD/CD have inhibitory
control problems independent of ADHD comorbidity. Previous reviews of inhibitory control and ODD/CD have only
focused on one type of measure of inhibitory control or used non-clinical samples. The current meta-analysis explored
inhibitory control problems of children with ODD/CD by systematically reviewing studies where children have a diagnosis
of ODD and/or CD. Comparisons were made across 25 studies between children with ODD/CD, ODD/CD + ADHD, ADHD,
and healthy controls (HC) on various measures of inhibitory control and ADHD symptomatology to explore impacts of
ADHD comorbidity. A small signicant eect (g = -0.58, p < .001) suggested children with ODD/CD are likely to have
more diculties with inhibitory control than healthy children. However, comparisons between clinical groups suggested
this eect may be due to ADHD symptomatology present in each group. As diculties with inhibitory control are similar,
across clinical groups, a dimensional approach to understanding ODD/CD and ADHD may be more useful to consider in
future diagnostic criteria. Similarities across clinical groups highlight that therapeutic approaches that assist children with
disruptive behaviours could benet from teaching children and their families how to cope with inhibitory control decits.
Keywords Inhibitory control · Conduct disorder · Oppositional deant disorder · Executive function · Disruptive behaviour
Introduction
Children with disruptive behaviour disorders have diculty
regulating emotions and inhibiting undesirable behaviours.
Executive function plays an important role in the regulation
of thoughts, emotions, and behaviours (Diamond 2013).
Recently, empirical studies have highlighted the unique role
that executive function decits can play in the aetiology of
disruptive behaviours (Ezpeleta and Granero 2014; Hobson
et al. 2011). Historically, these neurobiological factors have
often been overlooked in favour of psychological and social
factors that cause disruptive behaviour. According to the
Diagnostic and Statistical Manual (DSM-5), disruptive
behaviour disorders are classied as Disruptive, Impulse-
Control, and Conduct Disorders (DICCD); including
conduct disorder (CD), oppositional deant disorder (ODD),
kleptomania, intermittent explosive disorder, pyromania, and
other or unspecied disruptive, impulse-control and conduct
disorders (American Psychological Association 2013). In the
past, Attention Decit/Hyperactivity Disorder (ADHD) was
classied as a disruptive behaviour disorder, and as such,
much of the research on the relationship between executive
function deficits and disruptive behaviours has focussed
on children with ADHD. However, with the introduction
of the DSM-5, ADHD has been reclassified. It is now a
Neurodevelopmental Disorder due to empirical evidence
that neurobiological deficits (e.g., executive dysfunction)
are core characteristics of ADHD (Oosterlaan et al. 1998;
Thorell and Wahlstedt 2006; Senderecka et al. 2012).
Interestingly, similar deficits are present in early
childhood for children with ODD/CD (Schoemaker et al.
2012), but these disorders have remained in the DICCD
chapter. This systematic review uses a meta-analytic
* Mikaela D. Bonham
mikaela.bonham@grithuni.edu.au
1 School of Applied Psychology, Menzies Health
Institute of Queensland, Grith University, Mt Gravatt,
Quensland 4122, Australia
Research on Child and Adolescent Psychopathology
1 3
approach to examine the role of inhibition control, one of
the three key components of executive function, in ODD/
CD. It compares the following groups on measures of
inhibitory control as well as ADHD symptomatology: ODD/
CD, ODD/CD + ADHD, ADHD, and healthy controls (HC).
Systematically reviewing the available empirical evidence
will help us to consider whether ODD and CD are better
captured within diagnostic manuals as a neurodevelopmental
disorder.
Aetiology of ODD and CD
Psychopathology across the l i fespan typical ly
develops from the interaction between individual and
environmental factors over time (Matthys and Lochman
2017). When considering the aetiology and treatment of
disruptive behaviours, learned behaviour and parenting
style have received much attention. Children are
exposed to disruptive models of behaviour and learn
this behaviour from their environment, which in turn
develops into a disruptive behavioural disorder over time
(Tremblay 2010). This has been demonstrated in children
who model behaviour after coercive parents or associate
with delinquent peers when there is an absence of
positive parenting (Matthys et al. 2012). When disruptive
behaviour disorders are explained by coercive parenting
or peer inf luences, interventions naturally follow a
“learning-based” approach (Matthys et al., 2012, p.
235). Interventions focussed on parenting and behaviour
modification for antisocial youths have demonstrated
small to moderate effect sizes; with a mean effect size of
0.47 (range -0.06 to 1.68) and 0.35 (range -1.04 to 1.87),
respectively (McCart et al. 2006). Similarly, parenting
group interventions for externalising behaviours have also
demonstrated small to moderate effect sizes; with a mean
effect size of -0.38 favouring intervention (range -0.56
to -0.19; Buchanan-Pascall, Gray, Gordon & Melvin,
2018). Matthy and Lochman (2017) argue that although
there is demonstrated effectiveness for behavioural parent
training, the effect sizes remain small to moderate, which
may be due to many studies being conducted in highly
controlled environments which may not be representative
of real-world practice. Further, there may also be an
impact on children’s ability to learn and problem solve
due to neurocognitve impairments in areas such as
executive function (Matthys and Lochman, 2017; Matthys
et al., 2012). Matthys and colleagues (2012) highlighted
that the neurocognitive basis of skill deficits in children
with ODD and CD is understudied and understanding
its role in the development and maintenance of these
disorders has important implications for intervention,
with investigation into the role of executive function in
ODD/CD as being an important next step. Understanding
children’s neurocognitive challenges would be useful to
inform more individualised treatment for children and
their families (Matthys and Lochman, 2017). This review
will be the first to meta-analyse inhibitory control deficits
across childhood, in a clinical sample of children with
ODD/CD relative to healthy and clinical controls.
Inhibitory Control. Executive function deficits
are a key characteristic of ADHD. Inhibitory control
is one of the three established domains of executive
function (Miyake et al. 2000). Broadly, inhibitory
control refers to the ability to withhold an emotional or
behavioural response in order to achieve a goal (Best
and Miller 2010; Nigg 2000; van Goozen et al. 2004).
When there is a skill deficit in this area, children have
more difficulty stopping unwanted behaviour. While
this is the definition used in the present review, across
the literature, there are several ways of conceptualising
inhibitory control; definitions tend to differ based on
the function of inhibitory control. For example, some
have conceptualised inhibitory control as executive,
motivational, and attentional inhibitory control (Nigg
2000) or prepotent response inhibition, resistance to
distractor interference, and resistance to proactive
interference (Friedman and Miyake 2004).
The current review will examine cool and hot
inhibitory control separately because inhibitory control
may operate differently based on the emotional salience
of the task at hand (Zelazo and Carlson 2012; Zelazo
et al. 2010). Therefore, inhibitory control may operate
as a ‘hot’ function when a person is in affective contexts,
where cues for reward or punishment are present; for
example, a delayed snack task employing delayed
gratification (Zelazo et al. 2010). On the other hand,
‘cool’ inhibitory control is utilised when presented with
abstract problems (Zelazo et al. 2010); for example, a go/
no-go behavioural task, assessing a person’s ability to
not respond to a stimulus. Fundamentally, hot and cool
executive function require different cognitive processes
to be executed (Zelazo and Carlson 2012), which
suggests that tasks assessing hot and cool inhibitory
control should be analysed separately.
Measuring Inhibitory Control. Performance measures
and rating scales will be examined separately throughout
the review. Accurate measurement of executive function,
including inhibitory control is difficult due to the
overlapping nature of brain functions. It is impossible to
obtain a pure measure of one cognitive process, such as
inhibitory control, as all behaviours require more than
one cognitive process (Anderson 2002). This issue is
known as task impurity, where an outcome from a task or
measurement does not solely reect a single ability, because
completing the task requires more than one brain function
to be executed (Miyake and Friedman 2012). For example,
Research on Child and Adolescent Psychopathology
1 3
the Stroop task is a measure to assess inhibitory control
however the task requires reading and comprehension to be
completed. If an individual is impaired in either of these
areas, it may aect their performance on the task overall.
Performance measures of inhibitory control are standardised
or experimental tasks that capture a child’s ability to inhibit
a response when completing a task. Results of performance
measures of executive function are often confounded by
noise, as other executive and non-executive functions are
contributing to performance (Miyake et al. 2000; Miyake
and Friedman 2012). Ratng scales oer an ecological way to
assess inhibitory control by documenting informant- or self-
report of inhibitory control behaviours. They too continue
to be aected by task impurity. Further complicating the
measurement of inhibitory control, performance tasks and
rating scales have been demonstrated by some researchers
to not reect the same construct (Bodnar et al. 2007; Toplak
et al. 2013).
Meta-analysis may allow for a way to assess overall
inhibitory control performance at a group level, through
a pooled eect size across all measures, providing a more
global picture of inhibitory control in children with ODD/
CD. Analysing each task within inhibitory control separately
makes it dicult to ascertain an overall eect of inhibitory
control. However, understanding differences between
performance on each task of inhibitory control is important.
Prior meta-analyses of children with ADHD and ODD/CD
have only reviewed the Stop Task (Lipszyc and Schachar
2010; Oosterlaan et al. 1998). As such, the analyses in the
current review will pool all measures (i.e., according to
cool vs. hot, and performance measures vs. rating scales)
to understand inhibitory control in children with ODD/CD
more globally, as well as subgroup analyses by task.
The Relationship Between ODD/CD, ADHD,
and Inhibitory Control
This review will explore the similarities and dierences
between children with ODD/CD, ADHD, ODD/
CD + ADHD, and healthy controls on measures of inhibitory
control and ADHD symptomatology. When compared to
their typically developing peers, pre-schoolers with ADHD,
hard to manage behaviours, and aggressive behaviours have
been found to have more diculties with inhibitory control
(Schoemaker et al. 2013). However, there were too few
studies to conduct ADHD and disruptive behaviour analyses
separately. Oosterlaan and colleagues (1998) identied that
children with CD (and no ADHD) had more diculties
with inhibitory control compared to typically developing
children; however, the review was limited to one measure of
inhibitory control (i.e., stop signal task). Similarly, Lipszyc
and Schachar (2010) meta-analysed performance on the
stop signal task, revealing children with ODD/CD, ADHD,
and ADHD + ODD/CD had worse performance on the stop
signal task compared to healthy controls. Greatest eects
were found in children with ADHD, followed by ODD/CD
and ADHD + ODD/CD respectively. Similarly, others have
identied inhibitory control decits for children with ADHD
compared to healthy controls (e.g., Wright, Lipszyc, Dupuis,
Thayapararajah, Sathees, and Schachar 2014). There is an
absence of reviews where performance on measures of hot
inhibitory control has been assessed or reported for children
with ODD/CD.
Often, behavioural/response inhibition (i.e., cool
inhibitory control) is investigated in relation to externalising
behaviours such as ODD/CD. Poor response inhibition is
thought to be one factor that contributes to externalising
behaviour diculties; including conduct problems (Miyake
and Friedman 2012). As a result, children with diculties
in response inhibition would be expected to have more
diculty stopping unwanted behaviours (Hwang et al. 2016).
Others have identied more diculties with hot inhibitory
control, due to dysfunctional brain circuitry (cortico-striato-
thalamo-corticial neurocircuitry) responsible for emotional
executive function (Zhu et al. 2018). A review of imaging
studies indicated abnormalities in brain regions associated
with hot executive functions were more common in children
with ODD/CD compared to those with ADHD (Rubia 2011).
Further, diculties with emotional responding have been
observed in children aged 10 to 18 years, based on fMRI
data on an aective stop signal task (Hwang et al. 2016).
Despite the inhibitory control decit hypothesis for children
with ODD/CD, with a dearth of meta-analytic reviews, the
literature lacks consensus as to whether these decits are
characteristic of children with ODD/CD. Taken together,
our current understanding of inhibitory control deficits
in children with ODD/CD is limited; and this is further
complicated by the relationship between ODD/CD and
ADHD.
ADHD has often been found to be comorbid in girls
with CD (OR > 40) and ODD (OR = 79), and boys with
CD (OR = 3.7) and ODD (OR = 8.7; Costello et al. 2003).
Other studies have also identied high comorbidity between
CD and ADHD comorbidity in a community sample of
children (OR = 10.7Angold et al. 1999). As ODD/CD
and ADHD are often comorbid, it is dicult to ascertain
whether decits in inhibitory control are due to the severity
of disruptive behaviour in ODD/CD, or whether they may
simply be due to sub-clinical levels of ADHD symptoms
(Blair et al. 2018). For example, youths aged 10 to 18 years
with conduct disorder were found to have more diculties
with hot inhibitory control, however this was attributed to
the presence of ADHD symptoms, rather than the severity
of conduct problems (Hwang et al. 2016). Understanding
whether inhibitory control decits are indeed characteristic
of children with ODD/CD or if they are simply a result
Research on Child and Adolescent Psychopathology
1 3
of ADHD symptomatology may help us understand if
ODD/CD and ADHD are categorically dierent or similar
psychopathology within the same dimension.
Aims
The aim of this review was to comprehensively assess
whether children and adolescents (3–17 years) with a
clinical diagnosis of ODD and/or CD demonstrate inhibitory
control decits more than healthy peers, independent of
ADHD comorbidity. ODD/CD and ADHD are signicantly
comorbid, hence there is a need to examine whether
inhibitory control is similar or dierent for these diagnostic
categories. Therefore, this paper aims to determine whether
children with ODD/CD have greater or lesser inhibitory
control decits when compared to children with ADHD or
healthy controls (HC). We sought to answer these questions
by making comparisons between ODD/CD, ADHD, ODD/
CD + ADHD, and HC groups on measures of inhibitory
control and ADHD symptomatology. Additionally, we
explored whether there were differences between the
aforementioned groups on measures of cool and hot
inhibitory control, rating scales, and the implications of
measuring inhibitory control. All measures of inhibitory
control and ADHD symptomatology were included in
the review. Determining if inhibitory control decits are
characteristic of children with disruptive disorders will
contribute to a more comprehensive aetiological framework,
which in turn will inform more focussed intervention
strategies.
Methods
Inclusion and Exclusion Criteria
Studies were included if: (1) they were written in English,
(2) sample participants were 3-17 years old, (3) sample
participants had a clinical diagnosis of ODD, CD, and/or
comorbid ADHD based on ICD-10, DSM-IV, DSM-IV-TR,
or DSM-5 criteria using either clinical interviewing or
diagnostic measures, (4) outcome measures specically
tested for inhibitory control using a performance or rating
scale, and (5) they had a healthy control (HC) or ADHD
group as a comparison. Studies were excluded if sample
participants had intellectual impairment, Autism Spectrum
Disorder, or other cognitive impairments as comorbid
disorders.
Search Strategy and Study Selection
The meta-analysis followed the recommendations and
standards set by the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA; Moher et al. 2009).
A review protocol was registered with PROSPERO prior
to completion of title and abstract screening. The initial
protocol was amended, and all changes were reected on
the published PROSPERO protocol (CRD42019121527). To
obtain relevant literature, the following electronic databases
were accessed in February 2020: PsycINFO, PubMed,
Embase, CINAHL and Scopus. The nal title and abstract
searches were conducted using the strings:
(executive function OR cognitive function OR executive
dysfunction OR dysexecutive syndrome OR cognitive
dysfunction OR executive control OR executive
impairment OR inhibition OR inhibitory control OR
attentional control OR emotional control OR cognitive
control OR effortful control) AND (externalising
behaviour OR externalizing behavior OR oppositional
defiant disorder OR disruptive behaviour OR
disruptive behavior OR problem behaviour OR
problem behaviour OR conduct problems OR conduct
disorder OR ADHD OR AD/HD OR attention decit
hyperactivity disorder OR hyperkinetic) AND (child
OR children OR adolescent OR kid OR school OR
preschool OR pre-school OR pediatric OR paediatric
OR teen OR teenager OR youth OR boy OR girl).
Key words and MESH terms were determined to be
ineective in this search, as many records that were found
explored executive function as a secondary construct of
interest. Therefore, only title and abstract searches were
used. Searches did not employ a restriction of year of
publication. Reference list checks of review articles (Lipszyc
and Schachar 2010; Oosterlaan et al. 1998; Schoemaker
et al. 2013) and articles included in the current review were
hand-checked to ensure all possible eligible studies were
included.
Data Extraction
A total of 10,622 articles were retrieved through database
and reference list searches. Following deduplication and
assessment for eligibility, a total of 25 studies remained
for nal review. Screening and appraisal were completed
by two independent reviewers (MB and OE). Data
extraction was completed by MB and with cross checks
completed by another reviewer (OE). The following
variables were extracted from the data: sample size, age,
gender composition, country, IQ, primary diagnosis,
comorbid diagnoses, medication status, details of diagnostic
assessment, measures of inhibitory control, definition
of inhibitory control, dependent variable as a measure of
inhibitory control, measures of ADHD symptoms, eect
size, and confounding variables. Where a consensus could
not be reached, a third reviewer was consulted (DS). The
Research on Child and Adolescent Psychopathology
1 3
PRISMA ow diagram outlines assessment of studies in
Fig. 1.
Results
Studies Included
The following meta-analysis and synthesis will address
differences between a healthy control (HC) group and
children with a diagnosis of ODD/CD, ODD/CD + ADHD,
or ADHD. In total, 25 studies were considered for meta-
analysis (see Table 1), with the total included population
ranging from 3 to 14 years of age. Across the included
studies, eight dierent task paradigms were employed to
assess cool inhibitory, one hot inhibitory control measure
was used, and one type of rating scale. When reporting
ADHD symptoms, papers utilised standardised measures,
symptom scales, and symptom counts from diagnostic
interview schedules. Rating scales were either reported as a
T-score, subscale raw score, or an average item score.
Publication Bias and Quality Appraisal
Publication bias was assessed through visual inspection
of the funnel plot of standard error in Hedges G, revealing
Records idenfied
through database
searching February 2020
(n = 10, 619)
Sc
re
en
in
g
In
clu
de
d
El
ig
ib
ili
ty
Id
en
fi
ca
o
n Addional records
idenfied through
reference list checks
(n = 3)
Records aer duplicates removed
(n = 4298)
Records screened
(n = 4298)
Records excluded
(n = 4199)
Full-text arcles assessed
for eligibility
(n = 99)
Full-text arcles excluded (n=74)
Not in English (n=5)
Outside 3-17yrs (n=3)
No appropriate comparison (n=13)
Non-clinical sample ODD/CD (n = 14)
DSM-III (n= 10)
ASD/II/TBI (n= 1)
Did not assess inhibitory control (n=7)
Conference paper/dissertaon (n=15)
Duplicate sample/paper (n=2)
Unable to source data from authors (n=4)
Studies included in
qualitave synthesis
(n=25)
Studies included in
quantave synthesis
(meta-analysis)
(n = 25)
Fig. 1 PRISMA ow chart of studies included in the review
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
St
ud
y c
ha
ra
cte
ris
tic
s
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Al
br
ec
ht
et
al
.
( 2
00
5)
HC
=
11
OD
D/
CD
=
8
AD
HD
=
10
AD
HD
+
O
DD
/
CD
=
11
13
0.8
(1
8.9
)
13
1.5
(2
7.4
)
13
0.1
(1
8.0
)
12
3.7
(1
8.5
)
*r
ep
or
ted
in
m
on
th
s
Al
l m
ale
s
IC
D-
10
; v
er
i
ed
by
bo
ar
d c
er
ti
ed
ps
yc
hi
atr
ist
s
Cl
in
ica
l g
ro
up
s:
Re
ad
in
g a
nd
/o
r
sp
ell
in
g
di
so
rd
er
s
En
ur
es
is
En
co
pr
es
is
HC
: N
on
e
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
–
Pa
re
nt
R
ep
or
t:
At
ten
tio
n
Pr
ob
lem
s S
ca
le.
Re
po
rte
d
as
T
sc
or
es
No
t r
ep
or
ted
St
op
si
gn
al
tas
k;
M
ea
su
re
of
re
sp
on
se
in
hi
bi
tio
n
us
in
g
SS
RT
e
an
d S
to
p F
ail
ur
e
Re
ac
tio
n T
im
e
(R
T)
An
to
ni
ni
et
al
.
(2
01
5)
HC
=
30
AD
HD
+
O
DD
=
33
AD
HD
-O
DD
=
67
9.0
0(
1.8
0)
9.4
4(
1.7
5)
8.8
8(
1.4
8)
20
M
10
F
24
M
9F
50
M
17
F
DS
M
-IV
; K
id
di
e
Sc
he
du
le
fo
r
A
ec
tiv
e
Di
so
rd
er
s a
nd
Sc
hi
zo
ph
re
ni
a
fo
r S
ch
oo
l-
Ag
e C
hi
ld
re
n
– P
re
se
nt
an
d
Li
fet
im
e V
er
sio
n
(K
-S
AD
S-
PL
)
Cl
in
ica
l g
ro
up
s:
Sp
ec
i
c p
ho
bi
as
an
d s
ep
ar
ati
on
an
xi
ety
K-
SA
DS
: R
ep
or
ted
as
sy
m
pt
om
co
un
t
Ch
ild
re
n w
er
e
ex
clu
de
d i
f t
he
y
we
re
ta
ki
ng
an
y p
sy
ch
iat
ric
m
ed
ica
tio
n
Co
m
pu
ter
ize
d B
er
g
Ca
rd
S
or
tin
g T
es
t
(B
CS
T)
; M
ea
su
re
s
ab
ili
ty
to
in
hi
bi
t
a p
re
-p
ot
en
t
re
sp
on
se
th
ro
ug
h
to
tal
nu
m
be
r o
f
pr
es
er
va
tiv
e e
rro
rs
Ba
hc
iv
an
S
ay
da
m
et
al.
(2
01
5)
b
HC
=
36
AD
HD
+
O
DD
/
CD
=
37
AD
HD
Co
m
bi
ne
d =
37
AD
HD
In
at
ten
tiv
e =
37
9.3
3(
1.6
7)
9.1
4(
1.1
8)
8.9
5(
1.6
0)
9.8
9(
1.7
0)
30
M
6F
33
M
4F
31
M
6F
32
M
5F
DS
M
-IV
-T
R;
Cl
in
ica
l
in
ter
vi
ew
co
nd
uc
ted
w
ith
pa
re
nt
s a
nd
di
ag
no
se
d b
y a
ch
ild
ps
yc
hi
atr
ist
No
tr
ep
or
ted
Co
nn
er
s’
Pa
re
nt
an
d T
ea
ch
er
Ra
tin
g S
ca
le.
Re
po
rte
d
as
ra
w
sc
or
es
No
ch
ild
re
n
we
re
re
ce
iv
in
g
an
y
m
ed
ica
tio
n
St
ro
op
T
es
t;
M
ea
su
re
of
re
sp
on
se
in
hi
bi
tio
n.
As
se
ss
ed
by
th
e
co
m
pl
eti
on
ti
m
e
di
vi
de
d b
y t
he
du
ra
tio
n o
f n
am
in
g
th
e i
nk
co
lo
ur
Ba
na
sc
he
ws
ki
et
al.
(2
00
4)
HC
=
18
OD
D/
CD
=
15
HD
c =
15
HC
Dd =
16
10
.1(
1.5
)
10
.7(
1.8
)
9.9
(1
.6)
9.8
(1
.5)
16
M
2F
14
M
1F
14
M
1F
15
M
1F
IC
D-
10
; v
er
i
ed
by
bo
ar
d c
er
ti
ed
ps
yc
hi
atr
ist
s
Cl
in
ica
l g
ro
up
s:
Re
ad
in
g
di
so
rd
er
s
En
ur
es
is
En
co
pr
es
is
HC
: C
lin
ic
re
fer
re
d
fo
r d
ys
lex
ia
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
–
Pa
re
nt
R
ep
or
t:
At
ten
tio
n
Pr
ob
lem
s S
ca
le.
Re
po
rte
d
as
T
sc
or
es
Fr
ee
of
m
eth
yl
ph
en
id
ate
at
lea
st
48
hr
s
pr
io
r t
o t
es
tin
g
Cu
ed
C
on
tin
uo
us
Pe
rfo
rm
an
ce
tas
k C
PT
–
A–
X;
M
ea
su
re
of
re
sp
on
se
in
hi
bi
tio
n a
nd
m
ot
or
in
hi
bi
tio
n
(in
ter
ch
an
ge
ab
ly
re
fer
re
d t
o a
s
im
pu
lsi
vi
ty
).
M
ea
su
re
d
us
in
g
nu
m
be
r
of
re
sp
on
se
s t
o
no
n-
tar
ge
ts
at
cu
e
(“A
-n
ot
-X
)
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Bo
rk
ow
sk
a e
t a
l.
(2
01
6)
HC
=
47
OD
D
=
21
AD
HD
=
19
HF
A
=
21
NR 9.6
7(
1.1
1)
9.3
7(
1.5
3)
9.0
5(
1.5
3)
NR
NR
; C
hi
ld
re
n w
er
e
di
ag
no
se
d p
rio
r
to
th
e s
tu
dy
by
a
ch
ild
ps
yc
hi
atr
ist
or
ne
ur
ol
og
ist
, i
n
co
nj
un
cti
on
w
ith
a p
sy
ch
ol
og
ist
or
ot
he
r s
pe
cia
lis
t
No
co
m
or
bi
di
tie
s
No
t r
ep
or
ted
No
t r
ep
or
ted
M
OX
O-
CP
T;
T
o
id
en
tif
y d
e
cit
s
in
in
hi
bi
tio
n.
Re
po
rte
d
as
a m
ea
su
re
o
f
im
pu
lsi
ve
ne
ss
;
th
e n
um
be
r o
f
in
ap
pr
op
ria
te
re
sp
on
se
s t
o n
on
–
tar
ge
ts
Ez
pe
let
a a
nd
Gr
an
er
o (
20
15
)
HC
=
53
8
OD
D
=
51
OD
D
+
AD
HD
=
10
AD
HD
=
23
3.7
6(
0.3
3)
3.8
7(
0.3
0)
3.6
9(
0.3
1)
3.7
4(
0.3
3)
26
0 M
29
M
4 M 17
M
DS
M
-IV
-T
R;
Di
ag
no
sti
c
In
ter
vi
ew
fo
r
Ch
ild
re
n a
nd
Ad
ol
es
ce
nt
s
fo
r P
ar
en
ts
of
P
re
sc
ho
ol
Ch
ild
re
n (
DI
CA
–
PP
YC
)
To
tal
sa
m
pl
e:
AD
HD
, O
DD
,
CD
, d
ep
re
ss
io
n,
se
pa
ra
tio
n
an
xi
ety
, s
pe
ci
c
ph
ob
ia,
so
cia
l
ph
ob
ia
St
re
ng
th
s a
nd
Di
cu
lti
es
Qu
es
tio
nn
air
e:
AD
HD
S
ca
le
(P
ar
en
t a
nd
Te
ac
he
r R
ep
or
t).
Re
po
rte
d
as
ra
w
sc
or
es
No
t r
ep
or
ted
Be
ha
vi
ou
r R
ati
ng
In
ve
nt
or
y o
f
Ex
ec
ut
iv
e F
un
cti
on
pr
es
ch
oo
l v
er
sio
n
(B
RI
EF
-P
);
In
hi
bi
t
sc
ale
us
ed
to
as
se
ss
in
hi
bi
to
ry
co
nt
ro
l
Ki
dd
ie-
Co
nt
in
uo
us
Pe
rfo
rm
an
ce
T
as
k
(K
-C
PT
);
m
ea
su
re
s
re
sp
on
se
in
hi
bi
tio
n
th
ro
ug
h n
um
be
r o
f
co
m
m
iss
io
ns
Gl
en
n e
t a
l.
( 2
01
7)
CD
=
32
AD
HD
+
C
D
=
32
AD
HD
=
32
Ns
re
po
rte
d h
er
e a
re
th
os
e r
ep
or
ted
in
an
aly
sis
11
.44
(2
.05
)
11
.13
(1
.79
)
11
.03
(1
.89
)
87
.9%
M
89
.5%
M
85
.9%
M
DS
M
-IV
;
Di
ag
no
sti
c
In
ter
vi
ew
Sc
he
du
le
fo
r C
hi
ld
re
n
(C
-D
IS
C)
No
t r
ep
or
ted
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
– A
tte
nt
io
n
su
bs
ca
le.
Re
po
rte
d
as
a
raw
sc
or
e
Co
nn
er
s’
Pa
re
nt
Ra
tin
g S
ca
le
Re
vi
se
d:
S
ho
rt
Fo
rm
–
AD
HD
in
de
x.
Re
po
rte
d
as
ra
w
sc
or
e
60
.3%
of
th
e
sa
m
pl
e w
er
e
tak
in
g
sti
m
ul
an
t
m
ed
ica
tio
n
St
op
S
ig
na
lT
as
k;
M
ea
su
re
of
re
sp
on
se
in
hi
bi
tio
n
an
d i
m
pu
lse
co
nt
ro
l t
hr
ou
gh
SS
RT
e (a
n e
sti
m
ate
of
th
e l
en
gt
h o
f
tim
e b
etw
ee
n
th
e g
o
an
d s
to
p
sti
m
ul
i a
t w
hi
ch
th
e p
ar
tic
ip
an
t i
s
ab
le
to
in
hi
bi
t t
he
ir
re
sp
on
se
on
50
%
of
tr
ial
s)
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Gu
nt
he
r e
t a
l.
( 2
00
6)
HC
=
23
AD
HD
+
D
BD
=
23
11
.9(
2.2
)
11
.9(
2.1
)
18
M
5F
21
M
2F
DS
M
-IV
; K
in
de
r-
DI
PS
(K
-D
IP
S)
Cl
in
ica
l:
dy
sth
ym
ic
di
so
rd
er
, m
ajo
r
de
pr
es
sio
n,
an
xi
ety
di
so
rd
er
s
HC
: N
il
IO
W
A
Co
nn
er
s
ra
tin
g s
ca
le
– I
na
tte
nt
io
n-
Ov
er
ac
tiv
ity
sc
ale
. R
ep
or
ted
as
ra
w
sc
or
e
St
im
ul
an
ts
we
re
ce
as
ed
4
8h
ou
rs
pr
io
r t
o t
es
tin
g
Go
/N
o-
Go
; M
ea
su
re
of
re
sp
on
se
se
lec
tio
n/
in
hi
bi
tio
n
th
ro
ug
h t
he
nu
m
be
r o
f f
als
e
ala
rm
s
Ho
bs
on
et
al
.
(2
01
1)
HC
=
34
OD
D/
CD
=
28
AD
HD
±
O
DD
/
CD
=
31
13
.13
(1
.99
)
12
.64
(1
.98
)
13
.32
(1
.81
)
73
.53
%
M
67
.86
%
M
83
.87
%
M
DS
M
-IV
; C
hi
ld
an
d A
do
les
ce
nt
Ps
yc
hi
atr
ic
As
se
ss
m
en
t
(C
AP
A)
No
t r
ep
or
ted
Co
nn
er
s’
AD
HD
/
DS
M
-IV
P
ar
en
t
an
d T
ea
ch
er
Sc
ale
s.
Re
po
rte
d
as
T
sc
or
es
No
t r
ep
or
ted
fo
r
OD
D/
CD
AD
HD
g
ro
up
wa
s w
ith
ou
t
m
ed
ica
tio
n f
or
18
ho
ur
s p
rio
r t
o
tes
tin
g
Go
/N
o-
Go
; M
ea
su
re
of
m
ot
or
re
sp
on
se
in
hi
bi
tio
n t
hr
ou
gh
pe
rc
en
tag
e o
f
su
cc
es
sfu
lly
in
hi
bi
ted
no
-g
o
tri
als
St
op
T
as
k;
M
ot
or
re
sp
on
se
in
hi
bi
tio
n
m
ea
su
re
d t
hr
ou
gh
SS
RT
e (s
ub
tra
cti
ng
m
ea
n s
to
p s
ig
na
l
de
lay
fr
om
m
ea
n
re
ac
tio
n t
im
e t
o g
o
tri
als
)
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Hu
m
m
er
et
al
.
( 2
01
1)
HC
=
25
DB
Df =
23
DB
D
+
AD
HD
=
25
15
.1(
1.4
)
14
.80
(1
.3)
14
.7(
1.2
)
13
M
12
F
13
M
10
F
19
M
6F
DS
M
-IV
; S
ch
ed
ul
e
fo
r A
e
cti
ve
Di
so
rd
er
s a
nd
Sc
hi
zo
ph
re
ni
a
fo
r S
ch
oo
l-
Ag
ed
C
hi
ld
re
n,
Pr
es
en
t a
nd
Li
fet
im
e V
er
sio
n
(K
-S
AD
S)
AD
HD
+
O
DD
gr
ou
p:
G
AD
an
d s
ep
ar
ati
on
an
xi
ety
Ad
ol
es
ce
nt
Sy
m
pt
om
In
ve
nt
or
y –
4:
Co
m
bi
ne
d
AD
HD
sc
or
e.
Re
po
rte
d
as
T
sc
or
e
No
t r
ep
or
ted
St
ro
op
C
ol
ou
r W
or
d
Te
st;
In
hi
bi
tio
n
of
an
au
to
m
ati
c
re
sp
on
se
m
ea
su
re
d
by
nu
m
be
r o
f
co
m
pl
ete
d
ite
m
s
on
S
tro
op
C
ol
ou
r
W
or
d (
SC
W
)
Co
un
tin
g
In
ter
fer
en
ce
Te
st;
In
hi
bi
tio
n
of
an
au
to
m
ati
c
re
sp
on
se
m
ea
su
re
d
by
nu
m
be
r o
f
co
m
pl
ete
d
ite
m
s
Co
nn
er
’s
Co
nt
in
uo
us
Pe
rfo
rm
an
ce
Ta
sk
(C
CP
T)
; A
gr
ea
ter
nu
m
be
r
of
co
m
m
iss
io
ns
re
e
cts
po
or
er
in
hi
bi
to
ry
co
nt
ro
l
Be
ha
vi
ou
r R
ati
ng
In
ve
nt
or
y o
f
Ex
ec
ut
iv
e F
un
cti
on
(B
RI
EF
);
In
hi
bi
t
sc
ale
us
ed
to
as
se
ss
in
hi
bi
to
ry
co
nt
ro
l
Jia
ng
et
al
. (
20
16
)
HC
=
36
OD
D
=
7
OD
D
+
AD
HD
=
17
AD
HD
=
24
12
.92
(1
2.4
5–
13
.40
)
11
.76
(1
0.8
–1
2.6
8)
12
.64
(1
1.6
6–
13
.63
)
12
.17
(1
1.3
5–
13
.00
)
Re
po
rte
d a
s M
an
d
95
%
CI
s
HC 27
M
9F
Al
l O
DD
23
M
1F
AD
HD
22
M
2F
DS
M
-IV
; R
efe
rre
d
fro
m
ou
tp
ati
en
t
cli
ni
c d
ep
ar
tm
en
t
at
a m
en
tal
he
alt
h
ce
nt
re
No
co
m
bo
rb
id
iti
es
Co
nn
er
s P
ar
en
t
Sy
m
pt
om
s
Qu
es
tio
nn
air
e
(H
yp
er
ac
tiv
ity
an
d
Hy
pe
ra
cti
vi
ty
–
Im
pu
lsi
vi
ty
in
de
xe
s).
Re
po
rte
d
as
ra
w
sc
or
es
Dr
ug
na
ïv
e
St
ro
op
C
ol
ou
r W
or
d
Te
st;
m
ea
su
re
of
in
hi
bi
to
ry
co
nt
ro
l/c
ap
ab
ili
ty.
M
ea
su
re
s a
s t
he
co
rre
ct
nu
m
be
r i
n
th
e i
nt
er
fer
en
ce
zte
st
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Lu
m
an
et
al
( 2
00
9)
HC
=
50
AD
HD
+
O
DD
=
18
AD
HD
=
20
11
4(
15
)
11
8(
18
)
10
6(
17
)
*r
ep
or
ted
in
m
on
th
s
56
%
M
69
%
M
ac
ro
ss
cli
ni
ca
l g
ro
up
s
DS
M
-IV
;
Di
ag
no
sti
c
In
ter
vi
ew
S
ca
le
(D
IS
C-
IV
)
No
t r
ep
or
ted
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
an
d T
ea
ch
er
Ra
tin
g F
or
m
–
In
att
en
tio
n a
nd
Hy
pe
ra
cti
vi
ty
/
Im
pu
lsi
vi
ty
sc
ale
s.
Re
po
rte
d
as
ra
w
sc
or
es
DI
SC
-IV
sy
m
pt
om
co
un
t
– I
na
tte
nt
io
n
an
d
Hy
pe
ra
cti
vi
ty
/
Im
pu
lsi
vi
ty
M
eth
yl
ph
en
id
ate
ce
as
ed
at
le
as
t
24
h
pr
io
r t
o
tes
tin
g
St
op
T
as
k;
slo
we
r S
SR
Te
de
m
on
str
ate
s
in
hi
bi
tio
n
pr
ob
lem
s.
SS
RT
eq
ua
l t
o
th
e d
i
er
en
ce
s
be
tw
ee
n m
ea
n
re
ac
tio
n t
im
e o
n
go
-tr
ial
s a
nd
th
e
m
ea
n s
to
p s
ig
na
l
de
lay
M
ar
tel
et
al
. (
20
13
)
HC
=
24
OD
D
=
18
OD
D
+
AD
HD
=
39
AD
HD
=
17
3.7
9(
0.9
3)
4.5
6(
1.2
4)
4.4
9(
1.0
7)
4.5
3(
0.9
4)
To
tal
sa
m
pl
e
57
%M
DS
M
-IV
; K
id
di
e
Di
sru
pt
iv
e
Be
ha
vi
ou
r
Di
so
rd
er
s
Sc
he
du
le
(K
-D
BD
S)
No
t r
ep
or
ted
Di
sru
pt
iv
e
Be
ha
vi
ou
r R
ati
ng
Sc
ale
(D
BR
S)
– T
ea
ch
er
an
d
Ca
re
gi
ve
r r
ep
or
ts
In
att
en
tiv
e a
nd
Hy
pe
ra
cti
vi
ty
sc
ale
s r
ep
or
ted
as
ra
w
sc
or
es
Pa
re
nt
s w
er
e
en
co
ur
ag
ed
to
ce
as
e
ps
yc
ho
sti
m
ul
an
t
m
ed
ica
tio
n
24
–4
8 h
p
rio
r
to
te
sti
ng
if
ap
pr
op
ria
te.
No
on
e o
n
lo
ng
-a
cti
ng
ps
yc
ho
tro
pi
c
m
ed
ica
tio
n
Sh
ap
e S
ch
oo
l;
m
ea
su
re
of
re
sp
on
se
in
hi
bi
tio
n
th
ro
ug
h n
um
be
r
of
co
rre
ct
an
sw
er
s
di
vi
de
d b
y t
im
e t
o
co
m
pl
ete
th
e t
ria
l
Qi
an
et
al
. (
20
10
)
HC
=
11
6
AD
HD
+
O
DD
=
53
AD
HD
=
89
9.1
9(
1.6
2)
9.2
5(
1.7
9)
9.0
7(
1.9
2)
97
M
19
F
42
M
11
F
76
M
13
F
DS
M
-IV
; C
lin
ica
l
Di
ag
no
sti
c
In
ter
vi
ew
in
g
Sc
ale
(C
DI
S)
No
co
m
or
bi
di
tie
s
AD
HD
R
ati
ng
Sc
ale
(A
DH
D
RD
-IV
);
AD
HD
to
tal
sc
or
e
an
d c
om
pu
ted
in
att
en
tio
n a
nd
hy
pe
ra
cti
ve
/
im
pu
lsi
ve
sc
or
es
.
Re
po
rte
d
as
ra
w
sc
or
e
No
t r
ep
or
ted
Be
ha
vi
ou
r R
ati
ng
In
ve
nt
or
y o
f
Ex
ec
ut
iv
e F
un
cti
on
(B
RI
EF
);
In
hi
bi
t
sc
ale
us
ed
to
as
se
ss
in
hi
bi
to
ry
co
nt
ro
l
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Ru
bi
a e
t a
l.
( 2
00
8)
g
HC
=
20
CD
=
13
AD
HD
=
20
14
.0
(1
.9)
12
.9
(2
.2)
13
.2
(1
.4)
Al
l m
ale
DS
M
-IV
;
M
au
ds
ley
Di
ag
no
sti
c
In
ter
vi
ew
No
co
m
or
bi
di
tie
s
St
re
ng
th
s a
nd
Di
cu
lti
es
Qu
es
tio
nn
air
e
– H
yp
er
ac
tiv
ity
Sc
or
e.
Re
po
rte
d
as
ra
w
sc
or
e
Ex
clu
de
d i
f
pr
ev
io
us
ly
ex
po
se
d t
o
sti
m
ul
an
t
m
ed
ica
tio
n
St
op
T
as
k;
M
ot
or
re
sp
on
se
in
hi
bi
tio
n
m
ea
su
re
d t
hr
ou
gh
SS
RT
e (s
ub
tra
cti
ng
m
ea
n s
to
p s
ig
na
l
de
lay
fr
om
m
ea
n
re
ac
tio
n t
im
e t
o g
o
tri
als
)
Ru
bi
a e
t a
l.
(2
00
9)
g
HC
=
20
CD
=
13
AD
HD
=
20
14
(2
)
13
(1
)
13
.2
(1
.5)
Al
l m
ale
DS
M
-IV
;
M
au
ds
ley
Di
ag
no
sti
c
In
ter
vi
ew
No
co
m
or
bi
di
tie
s
St
re
ng
th
s a
nd
Di
cu
lti
es
Qu
es
tio
nn
air
e
– H
yp
er
ac
tiv
ity
Sc
or
e.
Re
po
rte
d
as
ra
w
sc
or
e
Ex
clu
de
d i
f
pr
ev
io
us
ly
ex
po
se
d t
o
sti
m
ul
an
t
m
ed
ica
tio
n
Si
m
on
T
as
k;
In
hi
bi
tio
n o
f a
n
in
co
rre
ct
re
sp
on
se
to
in
co
ng
ru
en
t
sti
m
ul
i.
M
ea
su
re
d
by
re
sp
on
se
ti
m
es
to
in
co
ng
ru
en
t
co
m
pa
re
d t
o
co
ng
ru
en
t t
ria
ls
(C
on
i
ct
Re
ac
tio
n
Ti
m
e e
e
ct)
Sa
br
y e
t a
l.
(2
01
1)
h
HC
=
45
AD
HD
in
at
ten
tiv
e =
14
AD
HD
hy
pe
ra
cti
ve
=
15
AD
HD
co
m
bi
ne
d =
15
CD
=
24
OD
D
=
13
Bi
po
lar
=
11
9.1
(1
.8)
8.1
7(
1.8
)
8.9
(2
.07
)
8.2
(1
.4)
9.9
(1
.8)
8.1
(1
.9)
11
.1(
0.7
)
25
M
20
F
9 M
5F
12
M
3F
12
M
3F
16
M
8F
6 M
7F
5 M
6F
DS
M
-IV
;
di
ag
no
se
d b
y
tw
o i
nd
ep
en
de
nt
ps
yc
hi
atr
ist
s
us
in
g
a s
em
i-
str
uc
tu
re
d
in
ter
vi
ew
gu
id
ed
by
C
hi
ld
M
en
tal
S
tat
us
Ex
am
in
ati
on
No
co
m
or
bi
di
tie
s
No
t r
ep
or
ted
No
t r
ep
or
ted
Qu
an
tit
yi
nh
ib
iti
on
tas
k;
nu
m
be
r o
f
an
sw
er
s c
or
re
ct
Ob
jec
t i
nh
ib
iti
on
tas
k;
nu
m
be
r o
f
an
sw
er
s c
or
re
ct
Nu
m
er
ica
l s
ize
in
hi
bi
tio
n t
as
k;
nu
m
be
r o
f a
ns
we
rs
co
rre
ct
Sc
ha
ch
ar
et
al
.
(2
00
0)
HC
=
33
CD
=
13
AD
HD
=
72
AD
HD
+
C
D
=
47
9.3
(1
.5)
9.5
(1
.4)
9.0
(1
.4)
9.2
(1
.5)
3:
2
6:
1
4:
1
9:
1
*m
:f
ra
tio
DS
M
-IV
; P
ar
en
t
In
ter
vi
ew
fo
r
Ch
ild
S
ym
pt
om
s
(P
IC
S)
an
d
Te
ac
he
r
Te
lep
ho
ne
In
ter
vi
ew
(T
TI
)
To
tal
sa
m
pl
e:
Ex
clu
de
d i
f
ps
yc
ho
sis
,
or
cl
in
ica
lly
sig
ni
c
an
t
m
oo
d o
r a
nx
iet
y
di
so
rd
er
p
re
se
nt
HC
: F
re
e o
f
co
m
or
bi
d
di
ag
no
se
s
Pa
re
nt
In
ter
vi
ew
fo
r C
hi
ld
Sy
m
pt
om
s
an
d T
ea
ch
er
Te
lep
ho
ne
In
ter
vi
ew
.
Re
po
rte
d
as
ra
w
sc
or
es
St
im
ul
an
t
m
ed
ica
tio
n
ce
as
ed
4
8h
rs
pr
io
r t
o t
es
tin
g
St
op
-si
gn
al;
D
e
cit
s
of
in
hi
bi
to
ry
co
nt
ro
l m
ea
su
re
d
th
ro
ug
h
SS
RT
e
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Sc
ho
em
ak
er
et
al
.
( 2
01
2)
HC
=
56
DB
Df =
33
AD
HD
=
61
AD
HD
+
D
BD
=
52
55
.66
(7
.18
)
51
.88
(8
.29
)
55
.20
(7
.41
)
54
.12
(6
.80
)
*r
ep
or
ted
in
m
on
th
s
69
.6%
M
81
.8%
M
80
.3%
M
82
.7%
M
DS
M
-IV
-T
R;
Co
ns
en
su
s
be
tw
ee
n c
hi
ld
ps
yc
hi
atr
ist
an
d
cli
ni
ca
l c
hi
ld
ps
yc
ho
lo
gi
st
us
in
g
sy
m
pt
om
m
ea
su
re
s
an
d c
lin
ica
l
in
ter
vi
ew
in
g
No
ne
re
po
rte
d
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
–
Pa
re
nt
an
d
Te
ac
he
r R
ep
or
t:
At
ten
tio
n
Pr
ob
lem
s S
ca
le.
Re
po
rte
d
as
T
sc
or
es
No
ch
ild
re
n
we
re
on
m
ed
ica
tio
n
Go
/N
o-
Go
; M
ea
su
re
of
in
hi
bi
to
ry
sk
ill
s t
hr
ou
gh
th
e
nu
m
be
r o
f N
o-
Go
tri
als
co
rre
ctl
y n
ot
–
pr
es
se
d d
iv
id
ed
th
e t
ot
al
nu
m
be
r o
f
No
-G
o
tri
als
M
od
i
ed
S
na
ck
De
lay
; M
ea
su
re
of
in
hi
bi
to
ry
sk
ill
s t
hr
ou
gh
th
e n
um
be
r o
f
in
ter
va
ls
th
at
th
e
ch
ild
co
m
pl
ied
wi
th
al
l t
as
k r
ul
es
Sh
ap
e S
ch
oo
l –
In
hi
bi
t C
on
di
tio
n;
M
ea
su
re
of
in
hi
bi
to
ry
sk
ill
s
th
ro
ug
h n
um
be
r o
f
co
rre
ct
re
sp
on
se
s
di
vi
de
d b
y t
he
to
tal
nu
m
be
r o
f t
ria
ls
Sh
ua
i e
t a
l.
(2
01
1)
HC
=
76
AD
HD
+
O
DD
=
38
AD
HD
=
76
AD
HD
+
Le
ar
ni
ng
Di
so
rd
er
=
38
10
.21
(2
.30
)
10
.34
(2
.53
)
10
.24
(2
.40
)
10
.38
(2
.56
)
Al
l m
ale
DS
M
-IV
;
St
ru
ctu
re
d
in
ter
vi
ew
co
nd
uc
ted
by
ps
yc
hi
atr
ist
s
Ti
c a
nd
m
oo
d
di
so
rd
er
s
No
t r
ep
or
ted
No
m
ed
ica
tio
n
pr
io
r t
o t
es
tin
g
St
ro
op
C
ol
ou
r a
nd
W
or
d T
es
t;
to
m
ea
su
re
in
hi
bi
tio
n
by
an
in
ter
fer
en
ce
sc
or
e
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
Sk
og
an
et
al
( 2
01
4)
HC
=
45
5
OD
D
=
20
5
AD
HD
+
O
DD
=
23
5
AD
HD
=
15
0
41
.7(
1.3
)
41
.8(
1.4
)
41
.7(
1.3
)
41
.5(
1.2
)
*r
ep
or
ted
in
m
on
th
s
23
9 M
21
6F
10
6 M
99
F
13
6 M
99
F
80
M
70
F
DS
M
-IV
-T
R;
Pr
es
ch
oo
l A
ge
Ps
yc
hi
atr
ic
As
se
ss
m
en
t
in
ter
vi
ew
(P
AP
A)
No
t r
ep
or
ted
PA
PA
sy
m
pt
om
ra
tin
gs
. R
ep
or
ted
as
a
sy
m
pt
om
co
un
t
Ni
l ps
yc
ho
sti
m
ul
an
t
m
ed
ica
tio
n
at
th
e t
im
e o
f
as
se
ss
m
en
t
NE
PS
Y
su
bt
es
t –
St
atu
e;
m
ea
su
re
of
in
hi
bi
tio
n.
Sc
or
in
g:
tw
o
po
in
ts
pe
r 5
-s
in
ter
va
l a
nd
po
in
ted
de
du
cte
d
fo
r m
ov
em
en
ts
pe
r
in
ter
va
l
Sp
in
th
e P
ot
s;
as
se
ss
es
ab
ili
ty
to
su
pp
re
ss
pr
ep
ot
en
t
re
sp
on
se
. R
ep
or
ted
as
an
im
pu
lsi
vi
ty
sc
or
e
Sk
og
an
et
al
.
( 2
01
5)
HC
=
11
7
OD
D
=
39
AD
HD
=
10
4
An
xi
ety
=
48
To
tal
sa
m
pl
e
41
.8(
1.3
)
*r
ep
or
ted
in
m
on
th
s
65
M
52
F
21
M
18
F
66
M
38
F
27
M
21
F
DS
M
-IV
;
Pr
es
ch
oo
l A
ge
Ps
yc
hi
atr
ic
As
se
ss
m
en
t
in
ter
vi
ew
(P
AP
A)
No
t r
ep
or
ted
PA
PA
sy
m
pt
om
ra
tin
gs
. R
ep
or
ted
as
a
sy
m
pt
om
co
un
t.
Re
po
rte
d
as
a
m
ea
n f
or
th
e
sa
m
pl
e o
ve
ra
ll
Ni
l ps
yc
ho
sti
m
ul
an
t
m
ed
ica
tio
n
at
th
e t
im
e o
f
as
se
ss
m
en
t
Be
ha
vi
ou
r R
ati
ng
In
ve
nt
or
y o
f
Ex
ec
ut
iv
e
Fu
nc
tio
n –
Pr
es
ch
oo
l v
er
sio
n
(B
RI
EF
-P
);
In
hi
bi
t
sc
ale
us
ed
to
as
se
ss
in
hi
bi
to
ry
co
nt
ro
l
Va
n G
oo
ze
n e
t a
l.
(2
00
4)
HC
=
36
OD
D
=
15
OD
D
+
AD
HD
=
26
9.2
(1
.2)
10
.1(
1.2
)
9.5
(1
.6)
14
M
22
F
36
M
5F
(T
ot
al
OD
D)
DS
M
-IV
; D
IS
C-
P
OD
D
gr
ou
p:
De
pr
es
sio
n,
an
xi
ety
, O
CD
,
To
ur
ett
es
/
tic
di
so
rd
er
,
en
ur
es
is/
en
co
pr
es
is
OD
D
+
AD
HD
gr
ou
p:
C
D,
de
pr
es
sio
n,
an
xi
ety
, O
CD
,
To
ur
ett
es
/ti
c
di
so
rd
er
, e
nu
re
sis
an
d e
nc
op
re
sis
HC
: A
DH
D
(n
=
1)
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
–
Pa
re
nt
an
d
Te
ac
he
r R
ep
or
t:
At
ten
tio
n
Pr
ob
lem
s S
ca
le.
Re
po
rte
d
as
T
sc
or
es
No
t r
ep
or
ted
St
ro
op
co
lo
ur
w
or
d
tes
t;
Re
qu
ire
s
in
hi
bi
tio
n o
f
an
ov
er
lea
rn
ed
au
to
m
ati
c
re
sp
on
se
.
M
ea
su
re
d v
ia
SC
W
T-
in
tti
m
e;
th
e t
im
e r
eq
ui
re
d
to
co
m
pl
ete
th
e
sti
m
ul
us
se
t i
n
th
e i
nt
er
fer
en
ce
co
nd
iti
on
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
1
(c
on
tin
ue
d)
Re
fer
en
ce
Su
bj
ec
ts
Ag
e a
M
(S
D)
Se
x
Di
ag
no
sti
c
cr
ite
ria
an
d
as
se
ssm
en
t
Re
po
rte
d
Co
m
or
bi
d
di
ag
no
se
s
AD
HD
S
ym
pt
om
M
ea
su
re
M
ed
ica
tio
n
sta
tu
s
Ta
sk
ch
ar
ac
ter
ist
ics
W
ier
se
m
a e
t a
l
( 2
00
6)
HC
=
15
AD
HD
+
O
DD
=
9
AD
HD
=
13
10
.2(
1.9
7)
To
tal
A
DH
D
10
.3(
1.5
9)
10
M
5F
To
tal
A
DH
D
14
M
8F
DS
M
-IV
;
Di
ag
no
sti
c
In
ter
vi
ew
Sc
he
du
le
fo
r C
hi
ld
re
n
(D
IS
C-
IV
)
No
t r
ep
or
ted
Ch
ild
B
eh
av
io
ur
Ch
ec
kl
ist
an
d
Te
ac
he
r R
ep
or
t
Fo
rm
Di
sru
pt
iv
e
Be
ha
vi
ou
r
Di
so
rd
er
R
ati
ng
Sc
ale
Re
su
lts
n
ot
re
po
rte
d
M
eth
yl
ph
en
id
ate
wa
s c
ea
se
d
fo
r a
t l
ea
st
24
h
pr
io
r t
o
tes
tin
g.
No
ot
he
r
m
ed
ica
tio
ns
u
se
d
Go
/N
o-
Go
ta
sk
;
co
rre
lat
es
of
in
hi
bi
to
ry
co
nt
ro
l,
as
se
ss
ed
by
pe
rc
en
tag
e
of
er
ro
rs
of
co
m
m
iss
io
n
Xu
et
al
. (
20
17
)
HC
=
52
OD
D
=
14
OD
D
+
AD
HD
=
29
AD
HD
=
39
10
.02
(2
.10
)
9.8
5(
1.9
1)
10
.11
(1
.74
)
9.1
6(
1.8
2)
Al
l m
ale
DS
M
-IV
; S
ch
ed
ul
e
fo
r A
e
cti
ve
Di
so
rd
er
s a
nd
Sc
hi
zo
ph
re
ni
a
fo
r S
ch
oo
l-
Ag
ed
C
hi
ld
re
n,
Pr
es
en
t a
nd
Li
fet
im
e V
er
sio
n
(K
-S
AD
S-
PL
)
OD
D:
N
o
co
m
or
bi
di
tie
s
Co
nn
er
s’
Pa
re
nt
Sy
m
pt
om
Qu
es
tio
nn
air
e
– h
yp
er
ac
tiv
ity
–
im
pu
lsi
vi
ty
in
de
x.
Co
nt
ro
l
gr
ou
p n
ot
as
se
ss
ed
.
Re
po
rte
d
as
ra
w
sc
or
es
Dr
ug
na
ïv
e
St
ro
op
co
lo
ur
w
or
d
tes
t;
In
hi
bi
to
ry
co
nt
ro
l a
bi
lit
y
m
ea
su
re
d b
y
nu
m
be
r o
f c
or
re
ct
re
ad
s i
n t
he
in
ter
fer
en
ce
te
st
a M
ea
n a
ge
an
d s
tan
da
rd
de
vi
ati
on
re
po
rte
d i
n y
ea
rs
un
les
s o
th
er
wi
se
sp
ec
i
ed
b A
DH
D
gr
ou
ps
fr
om
th
e B
ah
civ
an
S
ay
da
m
(2
01
5)
st
ud
y w
er
e p
oo
led
to
ge
th
er
re
sp
ec
tiv
ely
w
he
n c
on
sid
er
ed
fo
r m
eta
-a
na
lys
is
c H
yp
er
ki
ne
tic
D
iso
rd
er
d H
yp
er
ki
ne
tic
C
on
du
ct
Di
so
rd
er
e S
to
p s
ig
na
l r
ea
cti
on
ti
m
e,
f D
isr
up
tiv
e b
eh
av
io
ur
di
so
rd
er
s (
OD
D/
CD
)
g R
ub
ia
(2
00
8;
20
09
) u
til
ise
sa
m
e s
am
pl
e a
nd
da
ta
is
po
ol
ed
fo
r m
eta
-a
na
lys
is
h C
D
an
d
OD
D
gr
ou
ps
an
d
AD
HD
g
ro
up
s f
ro
m
th
e S
ab
ry
(2
01
1)
st
ud
y
we
re
p
oo
led
to
ge
th
er
re
sp
ec
tiv
ely
w
he
n
co
ns
id
er
ed
fo
r m
eta
-a
na
lys
is.
*
Gr
ou
ps
in
b
ol
d
ha
ve
b
ee
n
us
ed
in
th
e
na
l m
eta
–
an
aly
sis
.
Research on Child and Adolescent Psychopathology
1 3
apparent symmetry. This was also reected by the Trim and
Fill test which reected minimal change in the estimated
eect between groups (Duval and Tweedie 2000). Studies
included in the current review underwent a quality appraisal
by two independent reviewers utilising the Joanna Briggs
Institute Critical Appraisal Tools (Table 2; Moola et al.
2017). All studies in the review were deemed to be of
adequate quality and as such, were included for final
analysis.
Calculation of Effect Sizes
If papers reported on the same sample, the first paper
published was utilised as the primary study. Provided they
were dierent from the primary paper, additional measures
from subsequent papers were included. Data from these
papers were treated as one sample in the analyses. Two
papers (Bahcivan Saydam et al. 2015; Sabry et al. 2011)
reported data by subgroups. For the purpose of this meta-
analysis, a single eect size was calculated according to the
formula recommended by Borenstein (2009).
All extracted data were entered into and analysed by
Comprehensive Meta-Analysis (CMA) software (Version
3). Meta-analyses were conducted for four outcome
types: performance measures for cool inhibitory control,
performance measure for hot inhibitory control, rating
scales, and ADHD symptoms. Comparisons were made
between HC, ODD/CD, ODD/CD + ADHD, and ADHD
groups. Results are presented as a mean eect size, reected
as Hedges g with associated 95% condence intervals acting
as a common metric between studies. A random eects
model was utilised to account for greater between study
variance.
Between Study Variability and Outliers
Between-study variability was examined through a Q test
of heterogeneity. A signicant Q suggests that reasons for
variance between studies should be considered. For example,
outliers and potential moderators should be examined
through meta-regression or other appropriate methods
(Borenstein 2009). Sensitivity analyses were conducted
for each analysis where significant heterogeneity was
indicated; outliers are only reported if they were removed
from analyses.
Potential Moderators
Each study implemented a performance measure of
inhibitory control, however tasks usually varied between
studies. While all tasks are designed to assess the same
underlying construct of inhibitory control, we know that
there can be variability between tasks due to task impurity
(i.e., due to the nature of cognitive ability, performance
is aected by other cognitive processes depending on the
nature of the task; Anderson 2002). As such, exploratory
subgroup analyses were conducted as a function of task
paradigm (e.g., Stroop, Go/No-Go, Continuous Performance
Tasks) to explore dierence of eect size between tasks.
Additionally, rating scales were reported as mean item
score, subscale total, or T-score and ADHD symptoms
were reported as T-scores, raw scores, or symptom counts.
These variances between studies were also considered as a
potential moderator.
Age was considered as a moderator as ages of participants
ranged from approximately three years to 15 years. Age
was coded as a continuous variable, with weighted mean
age calculated for each study if not provided. Gender was
coded as a continuous variable, representing the percentage
of males in the sample and was calculated when required.
Medication was considered as a moderator, however most
studies ceased children’s medication prior to testing and as
such, was not included in moderation analyses.
Performance Measures for Cool Inhibitory Control
Following are the analyses for performance on measures
of cool inhibitory control. Eect size and heterogeneity
analyses are found in Table 3. Forest plots can be found in
the Supplementary materials.
ODD/CD vs. HC. Across 15 studies, children with
ODD/CD were found to have signicantly more diculties
with cool inhibitory control compared to healthy controls
(g = -0.58, p < 0.001); with signicant heterogeneity. Age
was not found to be a signicant moderator (p = 0.42), nor
was percentage of males in the sample (p = 0.89). However,
subgroup analyses revealed signicant overall eects for the
following tasks: Shape Shift (k = 2, g = -0.74, p < 0.001),
Go/No-Go (k = 2, g = -0.75, p < 0.001), Stop Task (k = 4,
g = 0.36, p = 0.17), and tasks employing the Stroop paradigm
(k = 5, g = -0.83, p = 0.046).,
ODD/CD vs. ADHD. A meta-analysis of 13 studies did
not reveal a signicant dierence between children with
ODD/CD and ADHD. Moderation analyses revealed that age
did not signicantly contribute to between study variance
(p = 0.68) and nor did percentage of males in the sample
(p = 0.51). Further exploratory subgroup analyses identied
the only task with a signicant dierence in performance
was the Statue Task (g = 0.38, p < 0.001), however only one
study utilised this assessment.
ODD/CD vs. ODD/CD + ADHD. Across 13 studies,
children with a comorbid diagnosis of ODD/CD + ADHD
were found to perform more poorly on tasks of inhibitory
control compared to those of a single ODD/CD diagnosis
(g = 0.18, p = 0.03). The data were found to be homogenous
and no follow-up moderation was required. Exploratory
Research on Child and Adolescent Psychopathology
1 3
Ta
bl
e
2
Q
ua
lit
y a
pp
ra
isa
l o
f i
nc
lu
de
d s
tu
di
es
St
ud
y
1.
W
er
e t
he
cr
ite
ria
fo
r i
nc
lu
sio
n i
n
th
e s
am
pl
e c
lea
rly
de
n
ed
?
2.
W
er
e t
he
st
ud
y
su
bj
ec
ts
an
d t
he
se
tti
ng
de
sc
rib
ed
in
de
tai
l?
3.
W
as
th
e e
xp
os
ur
e
(in
hi
bi
to
ry
co
nt
ro
l)
m
ea
su
re
d i
n a
va
lid
an
d r
eli
ab
le
wa
y?
4.
W
er
e o
bj
ec
tiv
e,
sta
nd
ar
d
cr
ite
ria
us
ed
fo
r
m
ea
su
re
m
en
t o
f t
he
co
nd
iti
on
(O
DD
/
CD
)?
5.
W
er
e
co
nf
ou
nd
in
g
fac
-
to
rs
id
en
ti
ed
?
6.
W
er
e s
tra
teg
ies
to
de
al
wi
th
co
nf
ou
nd
in
g
fac
to
rs
sta
ted
?
7.
W
er
e t
he
ou
tco
m
es
m
ea
su
re
d i
n a
va
lid
an
d r
eli
ab
le
wa
y?
8.
W
as
ap
pr
op
ria
te
sta
tis
tic
al
an
aly
sis
us
ed
?
Al
br
ec
ht
et
al
.
( 2
00
5)
+
+
+
?
+
+
+
+
An
to
ni
ni
et
al
.
(2
01
5)
+
+
+
+
+
+
+
+
Ba
hc
iv
an
et
al
.
(2
01
5)
+
+
+
?
-
-
+
+
Ba
na
sc
he
ws
ki
et
al
.
(2
00
4)
+
+
+
?
+
+
+
+
Bo
rk
ow
sk
a e
t a
l.
(2
01
6)
+
+
+
?
-
-
+
+
Ez
pe
let
a a
nd
Gr
an
er
o (
20
15
)
+
+
+
+
+
+
+
+
Gl
en
n e
t a
l.
(2
01
7)
+
+
+
+
+
+
+
+
Gu
nt
he
r e
t a
l.
(2
00
6)
+
+
+
+
+
+
+
+
Ho
bs
on
et
al
.
(2
01
1)
+
+
?
+
+
+
+
+
Hu
m
m
er
et
al
.
(2
01
1)
+
+
+
+
+
+
+
+
Jia
ng
et
al
. (
20
16
)
+
+
+
?
-
-
+
+
Lu
m
an
et
al
. (
20
09
)
+
+
+
+
+
+
+
+
M
ar
tel
et
al
. (
20
13
)
+
+
+
+
+
+
+
+
Qi
an
et
al
. (
20
10
)
+
+
+
+
+
+
+
+
Ru
bi
a e
t a
l.
(2
00
9)
+
+
+
+
+
+
+
+
Ru
bi
a e
t a
l.
(2
00
8)
+
+
+
+
+
+
+
+
Sa
br
y e
t a
l.
(2
01
1)
+
+
?
?
-
-
?
+
Sc
ha
ch
ar
et
al
.
(2
00
0)
+
+
+
?
+
+
+
+
Sc
ho
em
ak
er
et
al
.
(2
01
2)
+
+
+
+
+
+
+
+
Sh
ua
i e
t a
l.
(2
01
0)
+
+
+
?
+
+
+
+
Sk
og
an
et
al
.
(2
01
4)
+
+
+
+
+
+
+
+
Research on Child and Adolescent Psychopathology
1 3
subgroup analyses revealed the following tasks had
significant differences between groups: Continuous
Performance Task (CPT; k = 3, g = 0.56, p = 0.003), and
Statue task (k = 1, g = 0.51, p < 0.001).
ODD/CD + ADHD vs. ADHD. Pooling all measures of
16 studies did not reveal a signicant dierence between
children with ODD/CD + ADHD and ADHD only
(p = 0.88). Moderation analyses revealed that age did not
signicantly account for between study variance (p = 0.65)
and nor did gender (p = 0.66). Exploratory subgroup analysis
revealed that the CPT was the only task where a signicant
dierence in performance was observed (k = 2, g = -0.81,
p = 0.002); with poorer performance in children with ODD/
CD + ADHD.
ODD/CD + ADHD vs. HC. An analysis of 19 studies
revealed that children with a comorbid diagnosis of ODD/
CD and ADHD performed more poorly than healthy
controls (g = -0.47, p < 0.001). Children with ODD/
CD + ADHD were found to perform more poorly on most
tasks, including: CPT (k = 3, g = -0.49, p = 0.007), Spin the
Pots (k = 1, g = -0.29, p < 0.001), Statue (k = 1, g = -0.34,
p < 0.001), Stop Task (k = 4, g = -0.34, p 0.01), Go/No-Go
(k = 4, g = -0.75, p < 0.001), and the Stroop paradigm (k = 7,
g = -0.59, p < 0.001),
Performance Measures for Hot Inhibitory Control
Meta-analysis could not be conducted due to one study using
a measure assessing hot inhibitory control. Schoemaker et al.
(2012) administered the Modied Snack Delay as a measure
of inhibitory control in a motivationally salient context. All
three clinical groups (i.e., Disruptive Behaviour Disorders
(DBD), ADHD, and ADHD + DBD) were found to have
poorer inhibitory control than healthy controls (p < 0.01).
Signicant main eects of ADHD and DBD were found, as
well as a signicant interaction eect of ADHD and DBD.
Rating Scales
The analyses for group dierences on ratings of inhibitory
control are presented below. Eect size and heterogeneity
analyses are found in Table 4. All studies included in the
following analyses reported using the BRIEF as a measure
of inhibitory control. Due to so few studies included for each
analysis, follow-up moderation analyses were not conducted
where heterogeneity was signicant. Variability is likely due
to the limited number of studies included, as well as dierent
metrics utilised between studies (i.e., T-scores, raw scores,
item scores). Forest plots can be found in the Supplementary
materials.
ODD/CD vs. HC. Three studies revealed no signicant
dierences between children with ODD/CD and healthy
controls (p = 0.103). Although each study utilised the * I
tem
4
re
qu
ire
d u
se
of
a
cli
ni
ca
l i
nt
er
vi
ew
to
gu
id
e d
iag
no
sis
; s
tu
di
es
re
ce
iv
ed
an
“u
ns
ur
e”
if
o
nl
y d
iag
no
sti
c c
rit
er
ia
we
re
pr
ov
id
ed
(e
.g.
, D
SM
-IV
).
* I
tem
8
wa
s a
ss
es
se
d a
s t
o w
he
th
er
th
e a
na
lys
is
wa
s a
pp
ro
pr
iat
e f
or
th
e p
ap
er
’s
ow
n
re
se
ar
ch
qu
es
tio
n.
Se
e M
oo
la
et
al.
(2
01
7)
fo
r f
ul
l d
eta
ils
o
f t
he
cr
ite
ria
fo
r e
ac
h i
tem
.
Ta
bl
e
2
(c
on
tin
ue
d)
St
ud
y
1.
W
er
e t
he
cr
ite
ria
fo
r i
nc
lu
sio
n i
n
th
e s
am
pl
e c
lea
rly
de
n
ed
?
2.
W
er
e t
he
st
ud
y
su
bj
ec
ts
an
d t
he
se
tti
ng
de
sc
rib
ed
in
de
tai
l?
3.
W
as
th
e e
xp
os
ur
e
(in
hi
bi
to
ry
co
nt
ro
l)
m
ea
su
re
d i
n a
va
lid
an
d r
eli
ab
le
wa
y?
4.
W
er
e o
bj
ec
tiv
e,
sta
nd
ar
d
cr
ite
ria
us
ed
fo
r
m
ea
su
re
m
en
t o
f t
he
co
nd
iti
on
(O
DD
/
CD
)?
5.
W
er
e
co
nf
ou
nd
in
g
fac
-
to
rs
id
en
ti
ed
?
6.
W
er
e s
tra
teg
ies
to
de
al
wi
th
co
nf
ou
nd
in
g
fac
to
rs
sta
ted
?
7.
W
er
e t
he
ou
tco
m
es
m
ea
su
re
d i
n a
va
lid
an
d r
eli
ab
le
wa
y?
8.
W
as
ap
pr
op
ria
te
sta
tis
tic
al
an
aly
sis
us
ed
?
Sk
og
an
et
al
.
( 2
01
5)
+
+
+
+
+
+
+
+
Va
n G
oo
ze
n e
t a
l.
(2
00
4)
+
+
+
+
+
+
+
+
W
ier
se
m
a e
t a
l.
(2
00
6)
+
+
+
?
+
+
+
+
Xu
et
al
. (
20
17
)
+
+
+
?
+
+
+
+
Research on Child and Adolescent Psychopathology
1 3
same measure, a non-signicant dierence may be due to
dierences in reporting between each study; with Ezpeleta
(2015) reporting an average total raw score, Hummer (2011)
reported an average T-score, and Skogan (2015) an average
item score.
ODD/CD vs. ADHD. Two studies revealed that children
with ADHD had more diculties with inhibitory control
than children with ODD/CD (g = 0.97, p = 0.001). While het-
erogeneity was not signicant, the 95% condence intervals
suggest greater variability in the presence and severity of
parent-reported inhibitory control decits. As such, further
empirical studies are needed.
ODD/CD vs. ODD/CD + ADHD. The meta-analysis of
two studies showed that children with a comorbid diagnosis
of ODD/CD + ADHD were more likely to be rated as hav-
ing more diculties with inhibitory control compared to
children with ODD/CD only. However, due to few studies,
conclusions are tentative.
ODD/CD + ADHD vs. ADHD. Similarly, only two stud-
ies used the BRIEF to assess dierences between children
with ODD/CD + ADHD and ADHD (Ezpeleta, 2015; Qian,
2010). Whilst both studies employed the same metric (raw
scores on the Inhibit scale), no signicant dierences were
observed (p = 0.87).
ODD/CD + ADHD vs. HC. Three studies revealed that
children with ODD/CD + ADHD were rated as having more
diculties with inhibitory control compared to healthy con-
trols (g = -1.95, p = 0.001). Signicant heterogeneity was
observed; however, this is likely due to dierences in metrics
employed and few studies in the analysis.
ADHD Symptomatology
The analyses for group dierences on measures of ADHD
symptomatology are below. Eect size and heterogeneity
statistics are reported in Table 5. Forest plots can be found
in the Supplementary materials.
ODD/CD vs. HC. Across 12 studies, children with
ODD/CD were found to have signicantly more symptoms
of ADHD compared to healthy controls (g = -1.59,
p < 0.001). However, studies were found to be signicantly
heterogenous. Between measures, three types of metrics
Table 3 Eect size and heterogeneity analysis for cool inhibitory control performance measures
k = number of samples included
N = number of participants
g = Hedge’s g
CI = condence interval
I2 = proportion of variability between studies.
Eect Size Analysis Heterogeneity Analysis
Comparison k N g SE 95%CI Z-value p Q df (Q) I2 p
ODD/CD vs HC 15 1931 -0.58 0.16 -0.90 – -0.26 -3.54 < 0.001 93.76 14 85.07 < 0.001
ODD/CD vs ADHD 13 993 0.06 0.07 -0.09 – 0.20 0.80 0.43 13.34 12 10.05 0.035
ODD/CD vs ODD/CD + ADHD 13 1010 0.18 0.07 0.02 – 0.35 2.13 0.03 15.96 12 24.81 0.19
ODD/CD + ADHD vs ADHD 16 1458 -0.01 0.08 -0.17 – 0.15 -0.15 0.88 27.86 15 46.17 0.02
ODD/CD + ADHD vs HC 19 2414 -0.47 0.005 -0.60 – -0.34 -7.01 < 0.001 27.61 18 34.81 0.07
Table 4 Eect size and heterogeneity analysis for inhibitory control on rating scales of inhibitory control
k = number of samples included
N = number of participants
g = Hedge’s g
CI = condence interval
I2 = proportion of variability between studies.
Eect Size Analysis Heterogeneity Analysis
Comparison k N g SE 95%CI Z-value p Q df (Q) I2 p
ODD/CD vs HC 3 793 -0.81 0.50 -1.78 – 0.16 -1.63 0.10 34.44 2 94.19 < 0.001
ODD/CD vs ADHD 2 217 0.97 0.29 0.40 – 1.55 3.32 0.001 3.15 1 68.27 0.07
ODD/CD vs ODD/CD + ADHD 2 109 1.12 0.44 0.26 – 1.98 2.55 0.01 3.42 1 70.79 0.06
ODD/CD + ADHD vs ADHD 2 175 -0.10 0.61 -1.29 – 1.08 -0.17 0.87 8.54 1 88.29 0.003
ODD/CD + ADHD vs HC 3 767 -1.95 0.57 -3.07 – -0.83 -3.41 0.001 21.97 2 90.90 < 0.001
Research on Child and Adolescent Psychopathology
1 3
were reported: T-scores, subscale raw scores, and symptom
counts. Subgroup analyses were conducted across these
metrics, with T-scores (k = 6, g = -2.02, p < 0.001), symptom
counts (k = 3, g = -0.65, p = 0.02), and subscale raw scores
(k = 4, g = -1.43, p = 0.003) showing signicant dierences
between groups. Gender was found to signicantly moderate
eect size, with a greater percentage of boys in the sample
with effect size (ß = -0.05, p < 0.001). Age was not a
signicant moderator (p = 0.20).
ODD/CD vs. ADHD. A meta-analysis of 11 studies did
not reveal a signicant dierence in ADHD symptomatology
was observed between children with ODD/CD and ADHD
with all measures pooled (p = 0.29). Age was found to
signicantly contribute to between study variance (ß = -0.15,
p = 0.016), as was gender with eect size associated with
a greater percentage of boys in the sample (ß = -0.04,
p < 0.001). Exploratory subgroup analyses were conducted
to examine dierences between metrics reported, however
no signicant dierences were noted (all p > 0.052).
ODD/CD vs. ODD/CD + ADHD. Children with a
comorbid diagnosis of ODD/CD + ADHD were reported
to have signicantly more ADHD symptoms than children
with ODD/CD alone across 13 studies (g = 0.74, p = 0.004).
Studies were also found to be heterogenous; as such,
moderation analyses were conducted. Age was not found
to signicantly explain between study variance (p = 0.86),
however proportion of males in the sample was signicantly
associated with eect size (ß = -0.04, p < 0.001). However,
this may be due to the majority of studies that reported
on gender had more than 75% of males in the sample.
Subgroup analyses revealed measures reported as symptom
counts (k = 2, g = 1.46, p = 0.02), subscale raw scores (k = 5,
g = 0.49, p = 0.005), and T scores (k = 6, g = 0.72, p = 0.03)
indicated children with ODD/CD + ADHD showed greater
ADHD symptomatology.
ODD/CD + ADHD vs. ADHD. Across 16 studies,
children with ODD/CD + ADHD were reported to have
more ADHD symptomatology than children with ADHD
alone (g = -0.49, p < 0.001). Studies showed significant
heterogeneity. Age did not moderate between study variance
(p = 0.72), nor did gender (p = 0.06). Through subgroup
analyses, only subscale raw scores (k = 8, g = -0.71,
p = 0.001) and symptom counts (k = 3, g = -0.35, p = 0.008)
revealed a signicant dierence between groups.
ODD/CD + ADHD vs. HC. Meta-analysis of 19 studies
revealed children with a comorbid diagnosis of ODD/
CD + ADHD had signicantly more symptoms of ADHD
compared to healthy controls (g = -3.23, p < 0.001). Age
was not a signicant moderator of eect size (p = 0.40)
and nor was gender (p = 0.47). Subgroup analyses revealed
all metrics of assessing ADHD symptomatology indicated
significant differences between groups; T-scores (k = 6,
g = -3.16, p < 0.001), subscale raw scores (k = 6, g = -2.77,
p < 0.001), and symptom counts (k = 4, g = -4.49, p < 0.001).
Discussion
This meta-analysis explored whether children with a
diagnosis of ODD and/or CD had more decits of inhibitory
control compared to healthy controls, independent of
a diagnosis of ADHD. To the best of our knowledge,
this meta-analysis is the rst to assess inhibitory control
performance in a clinical sample of children with ODD/CD,
using a range of inhibitory control measurement approaches
in both hot and cool contexts, and also giving consideration
to ADHD symptomatology.
ADHD, ODD, and CD: Categorical Disorders
or Dimensions of the Same Pathology?
Overall, results across multiple meta-analyses demonstrated
that children with ODD/CD and ADHD have similar ADHD
symptomatology and performance on tasks of inhibitory
Table 5 Eect size and heterogeneity analysis for ADHD symptomatology between groups
k = number of samples included
N = number of participants
g = Hedge’s g
CI = condence interval
I2 = proportion of variability between studies.
Eect Size Analysis Heterogeneity Analysis
Comparison k N g SE 95%CI Z-value p Q df (Q) I2 p
ODD/CD vs HC 12 1715 -1.59 0.27 -2.11—-1.07 -6.02 < 0.001 136.77 11 91.92 < 0.001
ODD/CD vs ADHD 11 872 0.35 0.33 -0.30 – 1.00 1.05 0.29 159.88 10 93.75 < 0.001
ODD/CD vs ODD/CD + ADHD 13 1010 0.74 0.26 0.24 – 1.23 2.88 0.004 128.31 12 90.65 < 0.001
ODD/CD + ADHD vs ADHD 14 1322 -0.49 0.13 -0.73 – -0.25 -3.94 < 0.001 51.46 13 74.74 < 0.001
ODD/CD + ADHD vs HC 15 2148 -3.23 0.28 -3.79—-2.68 -11.44 < 0.001 164.63 14 91.50 < 0.001
Research on Child and Adolescent Psychopathology
1 3
control. These findings further contribute to current
discussions as to whether ADHD and ODD/CD are best
captured under a dimensional approach to psychopathology
(Frick and Nigg 2012; Wakschlag et al. 2018). Specically,
results found that: (1) when children with ODD were
compared to those with ADHD, there was no signicant
dierence in cool inhibitory control performance; (2) this
held true for the majority of tasks when subgroup analyses
were conducted; (3) there was no signicant dierence
between ODD/CD children and ADHD children on
measures of parent reported ADHD symptomatology; (4)
despite an absence of a clinical ADHD diagnosis, children
with ODD still had similar ADHD symptomatology to those
children with a clinical diagnosis of ADHD; (5) children
with a combined diagnosis of ODD/CD + ADHD were found
to have worse inhibitory control than children with ODD/
CD alone, but not signicantly dierent from children with
ADHD alone; (6) children with a comorbid diagnosis were
found to have signicantly greater ADHD symptomatology
compared to both ODD/CD and ADHD respectively. In
sum, it appears that children with ODD/CD and ADHD
have diculties with inhibitory control and similar ADHD
symptomatology. Future diagnostic manuals may need to
consider these diagnoses within a dimensional framework
of psychopathology; rather than a categorical framework.
Interestingly, previous authors have supported similar
conclusions. For example, Blair et. al (2018) suggested
that while children with conduct problems have inhibitory
control dysfunction, this is likely manifested as ADHD
symptoms. The previous meta-analysis on the Stop task by
Oosterlaan et al. (1998) revealed similar results. Compared
to healthy controls, children with CD were found to have
more difficulties with inhibitory control as indicated
by the stop signal reaction time (SSRT), however, no
signicant dierences were found between ADHD and CD,
or ADHD + CD and ADHD. It has also been previously
established that ADHD, ODD, and CD are often co-morbid
(Angold et al. 1999) and share common risk factors for
development such as decits in inhibitory control (Matthys
and Lochman 2017). Signicant comorbidity is challenging
for categorical approaches to diagnosis. When signicant
comorbidity exists, a dimensional approach can be more
clinically meaningful. Even for children with non-clinical
levels of disruptive behaviour, diculties with inhibitory
control have been demonstrated (Schoemaker et al. 2013;
Woltering et al. 2016). This would suggest that diculties
with inhibitory control have been present across varying
severities of disruptive behaviour. Researchers have
discussed the lack of utility in classifying ADHD, ODD, and
CD as separate disorders, highlighting that there is evidence
to support the three disorders sharing common aetiology
across a number of factors (not simply neurobiological
deficits), and as such, should be considered as related
disorders (e.g., Frick and Nigg 2012; Wakschlag et al. 2018;
Matthys and Lochman 2017).
Do Children with ODD/CD have Hot or Cool
Inhibitory Control Decits?
Unfortunately, only one study (Schoemaker et al. 2012)
utilised a measure of hot inhibitory control, and as such,
further meta-analyses could not be conducted. Previous
authors have suggested that disruptive behaviour (in
particular CD), is associated with hot inhibitory control
(Rubia 2011; Zhu et al. 2018). This makes theoretical
sense as children with ODD/CD often engage in more
risk-taking and rule-breaking behaviours than their healthy
peers and are less sensitive to reward processing (Matthys
and Lochman 2017). Tasks that involve hot executive
functions usually assess this behaviour with tasks that are
motivationally salient (Antonini et al. 2015). However, the
dearth of research utilising or reporting on performance
tasks that involve hot inhibitory control mean that no
empirical conclusions can be drawn yet.
A small signicant eect was found when comparing
measures of cool inhibitory control, suggesting that children
with ODD and/or CD may have more difficulties with
inhibitory control compared to healthy peers. Additionally,
despite varying tasks assessing inhibitory control, these
diculties persist for children with a clinical diagnosis of
ODD/CD. This result supports those found in the review by
Oosterlaan et al. (1998), which assessed inhibitory control
decits in CD samples, independent of ADHD; nding
that children with CD performed signicantly worse than
healthy controls. However, Oosterlaan et al. (1998) reviewed
performance on the Stop Signal Task only.
The Impact of How Inhibitory Control is Measured
The current review included papers that specifically
identified inhibitory control as a variable of interest.
However, measures such as the Continuous Performance
Task (CPT) have previously defined the same outcome
measure (number of commissions) as either an index
of attention or inhibitory control. One could argue that
inhibitory control subserves attention; however, the
conceptualisation of executive function and where each
cognitive process sits within the framework is an ongoing
theoretical discussion in the executive function literature
(Baggetta and Alexander 2016; Packwood et al. 2011).
Further, the Stop Signal and Stroop tasks were most often
used to assess cool inhibitory control across all comparisons
between groups. Fewer studies utilised the Go/No-Go
task, Continuous Performance Tasks, and the Statue and
Spin the Pots tasks were used the least. It is challenging to
make strong conclusions about dierences in performance
Research on Child and Adolescent Psychopathology
1 3
on these tasks when so few studies are utilising the same
measure. Additionally, it limits our understanding as to
which tasks contribute to an overall eect of inhibitory
control deficits between groups. Future research would
benet from including several measures of inhibitory control
to determine dierences between tasks. Unfortunately, these
diculties will always be inherent in the eld of cognitive
research, due to task impurity.
Strong conclusions cannot be drawn based on ratings
with ecological measures (i.e., BRIEF) due to the paucity
of studies. A maximum of three studies were included for
most comparisons, with each generally using a dierent
metric of the BRIEF. It is likely that the use of dierent
metrics (T-scores, average raw subscale scores, and average
item scores) contributed to greater variability between
studies. This highlights two important points. First, that
there is a need for further research using ecologically valid
measures of inhibitory control; as these measures translate
to behaviours observed in real world settings (Toplak et al.
2013). Second, that consistent use of metric is important to
eectively compare and replicate research. Where available,
T-scores are most meaningful as results can be compared
across measures. Overall, there is much work yet to be done
in the use of rating scales of executive function.
The Impact of Inhibitory Control Decits on Clinical
Interventions for ODD/CD Children
Explaining disruptive behaviours from a perspective of
skill decit, interacting with environmental factors (e.g.,
parenting style, peer inuences) highlights the importance
of utilising alternative approaches to intervention. For
example, children who display disruptive behaviours are
more likely to elicit unhelpful parenting responses (Burke
et al. 2008). Harsher parenting styles are known to be
associated with increases in disruptive behaviour; and so,
the cycle is perpetuated (Combs-Ronto et al. 2009). This
may be an important consideration as to why learning-
based interventions are not successful for some children
with ODD/CD. As such, alternative interventions such as
the Collaborative Problem Solving approach (Greene 2014;
Pollastri et al. 2019) may be useful as such approaches
consider neurodevelopmental dierences that may impact
upon behaviour. We can utilise such approaches to assist
children to cope with or compensate for the skill decit;
which may improve clinical outcomes beyond what they
would have been with only a learning-based intervention.
Limitations and Future Directions.
Limitations within the studies impact the quality of the meta-
analysis overall; including, small sample sizes, dierences
in methodology and design, and demographic (Borenstein
2009). While subgroup analyses allowed for further
examination between tasks, most tasks had fewer than ve
studies included. Underpowered analyses contribute to
greater variance between studies which cannot be adequately
explored through moderation due to so few studies. Further,
the meta-analyses are limited by the nature of the data
they assess; mean effect sizes. A more comprehensive
examination of inhibitory control between clinical groups,
moderated by ADHD symptoms, could be conducted via
an analysis of all individual data from each of the included
studies (Stewart and Clarke 1995). The use of individual
data collected from each study would allow for more
comprehensive and sophisticated analyses to investigate the
range of ODD/CD and ADHD symptomatology. This may
facilitate further understanding of a dimensional approach
to psychopathology for these disorders. Additionally, if all
individual data is collected, this may allow for T-scores to
be used in more analyses, providing a more standardised
approach. Future research into the aetiology of disruptive
behaviours may consider adapting the approach of utilising
individual participant data.
Conclusion
The present review is, to our knowledge, the first to
comprehensively assess inhibitory control performance
in children with ODD/CD compared to healthy controls,
children with ADHD, and ODD/CD + ADHD. Furthermore,
this meta-analysis has taken into consideration ADHD
symptomatology between groups. Importantly, in line with
contemporary models of psychopathology, this review
has highlighted the need to consider disruptive behaviour
pathology from a dimensional perspective; which paves
the way for future research and potential implications for
diagnosis and treatment. While the understanding of the
broader aetiological framework of disruptive behaviours
is limited, the present meta-analysis found that inhibitory
control decits contribute to the development of disruptive
behaviours.
Electronic supplementary material The online version of this article
(https:// doi. org/ 10. 1007/ s10802- 020- 00713-9) contains supplementary
material, which is available to authorized users.
Acknowledgements The rst author is a recipient of a Grith Univer-
sity postgraduate research scholarship. The authors would like to thank
Dr David Reilly, Dr Sheri Madigan, and Mr Daniel Sullivan for their
feedback on the analyses of this paper.
Author Contributions All authors contributed to the study conception
and design. Material preparation and data collection were performed
by Mikaela Bonham and Olivia Elvin. Analyses were performed and
Research on Child and Adolescent Psychopathology
1 3
the rst draft was written by Mikaela Bonham. All authors provided
feedback and approved the nal manuscript.
Compliance with Ethical Standards
Conflict of Interest The authors declare they have no conicts of in-
terest.
References
Albrecht, B., Banaschewski, T., Brandeis, D., Heinrich, H., &
Rothenberger, A. (2005). Response inhibition deficits in
externalizing child psychiatric disorders: An ERP-study with the
stop-task. Behavioral and Brain Functions, 1(1), 22–36. https://
doi. org/ 10. 1186/ 1744- 9081-1- 22
American Psychological Association (2013). Diagnostic and statistical
manual of mental disorders (DSM-5®): American Psychiatric
Publishing.
Anderson, P. J. (2002). Assessment and development of executive
function (EF) during childhood. Child Neuropsychology, 8,
71–82. https:// doi. org/ 10. 1076/ chin.8. 2. 71. 8724
Angold, A., Costello, E. J., & Erkanli, A. (1999). Comorbidity. Journal
of Child Psychology and Psychiatry, 40(1), 57–87. https:// doi. org/
10. 1111/ 1469- 7610. 00424
Antonini, T. N., Becker, S. P., Tamm, L., & Epstein, J. N. (2015). Hot
and cool executive functions in children with attention-decit/
hyperactivity disorder and comorbid oppositional deant disorder.
Journal of the International Neuropsychological Society, 21(8),
584–595. https:// doi. org/ 10. 1017/ S1355 61771 50007 52
Baggetta, P., & Alexander, P. A. (2016). Conceptualization and
operationalization of executive eunction. Mind, Brain, and
Education, 10(1), 10–33. https:// doi. org/ 10. 1111/ mbe. 12100
Bahcivan Saydam, R., Ayvasik, H. B., & Alyanak, B. (2015). Executive
functioning in subtypes of attention decit hyperactivity disorder.
Norosikiyatri Arsivi, 52(4), 386–392. https://doi.org/10.5152/npa.
2015. 8712
Banaschewski, T., Brandeis, D., Heinrich, H., Albrecht, B., Brunner,
E., & Rothenberger, A. (2004). Questioning inhibitory control
as the specic decit of ADHD - Evidence from brain electrical
activity. Journal of Neural Transmission, 111(7), 841–864. https://
doi. org/ 10. 1007/ s00702- 003- 0040-8
Best, J. R., & Miller, P. H. (2010). A developmental perspective on
executive function. Child Development, 81(6), 1641–1660. https://
doi. org/ 10. 1111/j. 1467- 8624. 2010. 01499.x
Blair, R., Veroude, K., & Buitelaar, J. (2018). Neuro-cognitive system
dysfunction and symptom sets: A review of fMRI studies in youth
with conduct problems. [Literature Review]. Neuroscience and
Biobehavioral Reviews, 91, 69–90. https:// doi. org/ 10. 1016/j.
neubi orev. 2016. 10. 022
Bodnar, L. E., Prahme, M. C., Cutting, L. E., Denckla, M. B., &
Mahone, E. M. (2007). Construct validity of parent ratings of
inhibitory control. Child Neuropsychology, 13(4), 345–362.
https:// doi. org/ 10. 1080/ 09297 04060 08998 67
Borenstein, M. (2009). Introduction to meta-analysis (1st ed., Vol.
Book, Whole). Chichester, U.K: John Wiley & Sons.
Borkowska, A. R. (2016). The dynamics of attentional and inhibitory
functions in the presence of distracting stimuli in children with
attention-decit/hyperactivity disorder, high-functioning autism
and oppositional defiant disorder. Psychiatria i Psychologia
Kliniczna, 16(2), 68–80. https:// doi. org/ 10. 15557/ PiPK. 2016. 0010
Buchanan-Pascall, S., Gray, K. M., Gordon, M., & Melvin, G. A.
(2018). Systematic review and meta-analysis of parent group
interventions for primary school children aged 4–12 years with
externalising and/or internalising problems. Child Psychiatry
and Human Development, 49, 244–267. https:// doi. org/ 10. 1107/
s10578- 017- 0745-9
Burke, J. D., Pardini, D. A., & Loeber, R. (2008). Reciprocal
relationships between parenting behavior and disruptive
psychopathology from childhood through adolescence. Journal
of Abnormal Child Psychology, 36(5), 679–692. https:// doi. org/
10. 1007/ s10802- 008- 9219-7
Combs-Ronto, L. A., Olson, S. L., Lunkenheimer, E. S., & Samero, A.
J. (2009). Interactions between maternal parenting and children’s
early disruptive behavior: Bidirectional associations across the
transition from preschool to school entry. Journal of Abnormal
Child Psychology, 37(8), 1151–1163. https:// doi. org/ 10. 1007/
s10802- 009- 9332-2
Costello, E. J., Mustillo, S., Erkanli, A., Keeler, G., & Angold, A.
(2003). Prevalence and development of psychiatric disorders in
childhood and adolescence. JAMA Psychiatry, 60(8), 837–844.
https:// doi. org/ 10. 1001/ archp syc. 60.8. 837
Diamond, A. (2013). Executive Functions. Annual Review
of Psychology, 64(1), 135–168. https:// doi. org/ 10. 1146/
annur ev- psych- 113011- 143750
Duval, S., & Tweedie, R. (2000). Trim and ll: A simple funnel-plot-
based method of testing and adjusting for publication bias in meta-
analysis. Biometrics, 56(2), 455–463. https:// doi. org/ 10. 1111/j.
0006- 341x. 2000. 00455.x
Ezpeleta, L., & Granero, R. (2014). Executive functions in preschoolers
with ADHD, ODD, and comorbid ADHD-ODD: Evidence
from ecological and performance-based measures. Journal of
Neuropsychology, 9(2), 258–270. https:// doi. org/ 10. 1111/ jnp.
12049
Frick, P. J., & Nigg, J. T. (2012). Current issues in the diagnosis of
attention deficit hyperactivity disorder, oppositional defiant
disorder, and conduct disorder. Annual Review of Clinical
Psychology, 8(1), 77–107. https:// doi. org/ 10. 1146/ annur ev-
clinp sy- 032511- 143150
Friedman, N. P., & Miyake, A. (2004). The relations among inhibition
and interference control functions: A latent-variable analysis.
Journal of Experimental Psychology: General, 133(1), 101–135.
https:// doi. org/ 10. 1037/ 0096- 3445. 133.1. 101
Glenn, A. L., Remmel, R. J., Ong, M. Y., Lim, N. S. J., Ang, R. P.,
Threadgill, A. H., et al. (2017). Neurocognitive characteristics of
youth with noncomorbid and comorbid forms of conduct disorder
and attention deficit hyperactivity disorder. Comprehensive
Psychiatry, 77, 60–70. https:// doi. org/ 10. 1016/j. compp sych. 2017.
06. 005
Greene, R. W. (2014). The explosive child: a new approach for
understanding and parenting easily frustrated, chronically
inexible children (Fifth ed., Vol. Book, Whole). New York:
Harper.
Gunther, T., Herpertz-Dahlmann, B., Jolles, J., & Konrad, K. (2006).
The inuence of risperidone on attentional functions in children
and adolescents with attention-decit/hyperactivity disorder and
co-morbid disruptive behavior disorder. Journal of Child and
Adolescent Psychopharmacology, 16(6), 725–735. https:// doi.
org/ 10. 1089/ cap. 2006. 16. 725
Hobson, C., Scott, S., & Rubia, K. (2011). Investigation of cool and hot
executive function in ODD/CD independently of ADHD. Journal
of Child Psychology and Psychiatry, 52(10), 1035–1043. https://
doi. org/ 10. 1111/j. 1469- 7610. 2011. 02454.x
Hummer, T. A., Kronenberger, W. G., Wang, Y., Dunn, D. W.,
Mosier, K. M., Kalnin, A. J., et al. (2011). Executive functioning
characteristics associated with ADHD comorbidity in adolescents
with disruptive behavior disorders. Journal of Abnormal
Child Psychology, 39(1), 11–19, doi: https:// doi. org/ 10. 1007/
s10802- 010- 9449-3.
Research on Child and Adolescent Psychopathology
1 3
Hwang, S., Nolan, Z. T., White, S. F., Williams, W. C., Sinclair, S., &
Blair, R. J. (2016). Dual neurocircuitry dysfunctions in disruptive
behavior disorders: Emotional responding and response inhibition.
Psychological Medicine, 46(7), 1485–1496. https:// doi.org/ 10.
1017/ S0033 29171 60001 18
Jiang, W., Li, Y., Du, Y., & Fan, J. (2016). Emotional regulation and
executive function decits in unmedicated Chinese children with
oppositional deant disorder. Psychiatry Investigation, 13(3),
277–287. https:// doi. org/ 10. 4306/ pi. 2016. 13.3. 277
Lipszyc, J., & Schachar, R. (2010). Inhibitory control and
psychopathology: A meta-analysis of studies using the stop signal
task. Journal of the International Neuropsychological Society,
16(6), 1064–1076. https:// doi. org/ 10. 1017/ S1355 61771 00008 95
Luman, M., van Noesel, S. J., Papanikolau, A., Van Oostenbruggen-
Scheer, J., Veugelers, D., Sergeant, J. A., et al. (2009). Inhibition,
reinforcement sensitivity and temporal information processing in
ADHD and ADHD+ODD: Evidence of a separate entity? Journal
of Abnormal Child Psychology, 37(8), 1123–1135. https:// doi. org/
10. 1007/ s10802- 009- 9334-0
Martel, M. M., Roberts, B., & Gremillion, M. L. (2013). Emerging
control and disruptive behavior disorders during early childhood.
Developmental Neuropsychology, 38(3), 153–166. https:// doi. org/
10. 1080/ 87565 641. 2012. 758731
Matthys, W., & Lochman, J. E. (2017). Oppositional defiant
disorder and conduct disorder in childhood. United Kingdom:
Wiley-Blackwell.
Matthys, W., Vanderschuren, L. J., & Schutter, D. J. (2013). The
neurobiology of oppositional defiant disorder and conduct
disorder: Altered functioning in three mental domains.
Development and Psychopathology, 25(1), 193–207. https:// doi.
org/ 10. 1017/ S0954 57941 20002 72
Matthys, W., Vanderschuren, L. J., Schutter, D. J., & Lochman,
J. E. (2012). Impaired neurocognitive functions aect social
learning processes in oppositional deant disorder and conduct
disorder: Implications for interventions. Clinical Child Family
Psychology Review, 15(3), 234–246. https:// doi. org/ 10. 1007/
s10567- 012- 0118-7
McCart, M. R., Priester, P. E., Davies, W. H., & Azen, R. (2006).
Differential effectiveness of behavioral parent-training and
cognitive-behavioral therapy for antisocial youth: A meta-
analysis. [journal article]. Journal of Abnormal Child Psychology,
34(4), 525–541, doi:https:// doi. org/ 10. 1007/ s10802- 006- 9031-1.
Miyake, A., & Friedman, N. P. (2012). The nature and organization
of individual dierences in executive functions: Four general
conclusions. Current Directions in Psychological Science, 21(1),
8–14. https:// doi. org/ 10. 1177/ 09637 21411 429458
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter,
A., & Wager, T. D. (2000). The unity and diversity of executive
functions and their contributions to complex “frontal lobe” tasks:
A latent variable analysis. Cognitive Psychology, 41(1), 49–100.
https:// doi. org/ 10. 1006/ cogp. 1999. 0734
Moher, D., Liberati, A., Tetzla, J., & Altman, D. G. (2009). Preferred
reporting items for systematic reviews and meta-analyses: The
PRISMA statement. BMJ, 339(7716), 332–336. https:// doi. org/
10. 1136/ bmj. b2535
Moola, S., Munn, Z., Tufanaru, C., Aromataris, E., Sears, K., Sfetcu,
R., et al. (2017). Systematic reviews of etiology and risk. In E.
Aromataris, & Z. Munn (Eds.), Joanna Briggs Institute reviewer’s
manual: The Joanna Briggs Institute.
Nigg, J. T. (2000). On inhibition/disinhibition in developmental
psychopathology: Views from cognitive and personality
psychology and a working inhibition taxonomy. Psychological
Bulletin, 126(2), 220–246. https:// doi. org/ 10. 1037/ 0033-2909.
126.2. 220
Oosterlaan, J., Logan, G. D., & Sergeant, J. A. (1998). Response
inhibition in AD/HD, CD, comorbid AD/HD + CD, anxious, and
control children: a meta-analysis of studies with the stop task.
Journal of Child Psychology and Psychiatry, 39(3), 411–425.
https:// doi. org/ 10. 1017/ S0021 96309 70020 72
Packwood, S., Hodgetts, H. M., & Tremblay, S. (2011). A
multiperspective approach to the conceptualization of executive
functions. Journal of Clinical and Experimental Neuropsychology,
33(4), 456–470. https:// doi. org/ 10. 1080/ 13803 395. 2010. 533157
Pollastri, A. R., Ablon, J. S., & Hone, M. J. G. (2019). Collaborative
Problem Solving: An evidence-based approach to implementation
and practice (Vol. Book, Whole). Cham: Springer.
Qian, Y., Shuai, L., Cao, Q., Chan, R. C., & Wang, Y. (2010). Do
executive function decits dierentiate between children with
attention deficit hyperactivity disorder (ADHD) and ADHD
comorbid with oppositional deant disorder? A cross-cultural
study using performance-based tests and the behavior rating
inventory of executive function. Clinical Neuropsychologist,
24(5), 793–810. https:// doi. org/ 10. 1080/ 13854 04100 37493 42
Rubia, K. (2011). “Cool” inferior frontostriatal dysfunction
in attention-deficit/hyperactivity disorder versus “hot”
ventromedial orbitofrontal-limbic dysfunction in conduct
disorder: A review. Biological Psychiatry, 69(12), e69–e87.
https:// doi. org/ 10. 1016/j. biops ych. 2010. 09. 023
Rubia, K., Halari, R., Smith, A. B., Mohammad, M., Scott, S., &
Brammer, M. J. (2009). Shared and disorder-specic prefrontal
abnormalities in boys with pure attention-decit/hyperactivity
disorder compared to boys with pure CD during interference
inhibition and attention allocation. Journal of Child Psychology
and Psychiatry and Allied Disciplines, 50(6), 669–678. https://
doi. org/ 10. 1111/j. 1469- 7610. 2008. 02022.x
Rubia, K., Halari, R., Smith, A. B., Mohammed, M., Scott, S.,
Giampietro, V., et al. (2008). Dissociated functional brain
abnormalities of inhibition in boys with pure conduct disorder
and in boys with pure attention decit hyperactivity disorder.
American Journal of Psychiatry, 165(7), 889–897. https:// doi.
org/ 10. 1176/ appi. ajp. 2008. 07071 084
Sabry, Y., El-Boraie, H. A., El-Hadidy, M. E., El-Nagar, S., &
Khater, M. (2011). Cognitive dysfunction in children presented
with behavioral disorders. Middle East Current Psychiatry,
18(3), 177–184. https:// doi. org/ 10. 1097/ 01. XME. 00003 98871.
75584. cf
Schachar, R., Mota, V. L., Logan, G. D., Tannock, R., & Klim, P.
(2000). Conrmation of an inhibitory control decit in attention-
deficit/hyperactivity disorder. Journal of Abnormal Child
Psychology, 28(3), 227–235. https:// doi. org/ 10. 1023/a: 10051
40103 162
Schoemaker, K., Bunte, T., Wiebe, S. A., Espy, K. A., Dekovic, M.,
& Matthys, W. (2012). Executive function decits in preschool
children with ADHD and DBD. Journal of Child Psychology and
Psychiatry, 53(2), 111–119. https:// doi. org/ 10. 1111/j. 1469- 7610.
2011. 02468.x
Schoemaker, K., Mulder, H., Deković, M., & Matthys, W. (2013).
Executive functions in preschool children with externalizing
behavior problems: A meta-analysis. Journal of Abnormal
Child Psychology, 41(3), 457–471. https:// doi. org/ 10. 1007/
s10802- 012- 9684-x
Senderecka, M., Grabowska, A., Szewczyk, J., Gerc, K., & Chmylak,
R. (2012). Response inhibition of children with ADHD in the
stop-signal task: An event-related potential study. International
Journal of Psychophysiology, 85(1), 93–105. https:// doi. org/ 10.
1016/j. ijpsy cho. 2011. 05. 007
Shuai, L., Chan, R. C. K., & Wang, Y. (2010). Executive function
profile of Chinese boys with attention-deficit hyperactivity
disorder: Dierent subtypes and comorbidity. Archives of Clinical
Neuropsychology, 26(2), 120–132. https:// doi. org/ 10. 1093/ arclin/
acq101
Research on Child and Adolescent Psychopathology
1 3
Skogan, A. H., Zeiner, P., Egeland, J., Rohrer-Baumgartner, N.,
Urnes, A. G., Reichborn-Kjennerud, T., et al. (2014). Inhibition
and working memory in young preschool children with
symptoms of ADHD and/or oppositional-deant disorder. Child
Neuropsychology, 20(5), 607–624. https:// doi. org/ 10. 1080/
09297 049. 2013. 838213
Skogan, A. H., Zeiner, P., Egeland, J., Urnes, A. G., Reichborn-
Kjennerud, T., & Aase, H. (2015). Parent ratings of executive
function in young preschool children with symptoms of attention-
decit/-hyperactivity disorder. Behavioral Brain and Functions,
11(16), doi:https:// doi. org/ 10. 1186/ s12993-015- 0060-1.
Stewart, L. A., & Clarke, M. J. (1995). Practical methodology of
meta-analyses (overviews) using updated individual patient data.
Cochrane Working Group. Statistics in Medicine, 14(19), 2057–
2079. https:// doi. org/ 10. 1002/ sim. 47801 41902
Thorell, L., & Wahlstedt, C. (2006). Executive functioning decits
in relation to symptoms of ADHD and/or ODD in preschool
children. Infant and Child Development, 15, 503. https:// doi. org/
10. 1002/ icd. 475
Toplak, M. E., West, R. F., & Stanovich, K. E. (2013). Practitioner
review: Do performance-based measures and ratings of executive
function assess the same construct? Journal of Child Psychology
and Psychiatry, 54(2), 131–143. https:// doi. org/ 10. 1111/ jcpp.
12001
Tremblay, R. E. (2010). Developmental origins of disruptive behaviour
problems: The ‘original sin’ hypothesis, epigenetics and their
consequences for prevention. Journal of Child Psychology and
Psychiatry, 51(4), 341–367. https:// doi. org/ 10. 1111/j. 1469- 7610.
2010. 02211.x
van Goozen, S. H., Cohen-Kettenis, P. T., Snoek, H., Matthys, W.,
Swaab-Barneveld, H., & van Engeland, H. (2004). Executive
functioning in children: A comparison of hospitalised ODD and
ODD/ADHD children and normal controls. Journal of Child
Psychology and Psychiatry, 45(2), 284–292. https:// doi. org/ 10.
1111/j. 1469- 7610. 2004. 00220.x
Wakschlag, L. S., Perlman, S. B., Blair, R. J., Leibenluft, E., Briggs-
Gowan, M. J., & Pine, D. S. (2018). The neurodevelopmental
basis of early childhood disruptive behavior: Irritable and callous
phenotypes as exemplars. American Journal of Psychiatry,
175(2), 114–130. https:// doi. org/ 10. 1176/ appi. ajp. 2017. 17010 045
Wiersema, R., van der Meere, J., Roeyers, H., Van Coster, R., &
Baeyens, D. (2006). Event rate and event-related potentials in
ADHD. Journal of Child Psychology and Psychiatry, 47(6),
560–567. https:// doi. org/ 10. 1111/j. 1469- 7610. 2005. 01592.x
Woltering, S., Lishak, V., Hodgson, N., Granic, I., & Zelazo, P. D.
(2016). Executive function in children with externalizing and
comorbid internalizing behavior problems. Journal of Child
Psychology and Psychiatry, 57(1), 30–38. https:// doi. org/ 10.
1111/ jcpp. 12428
Wright, L., Lipszyc, J., Dupuis, A., Thayapararajah, S. W., & Schachar,
R. (2014). Response inhibition and psychopathology: A meta-
analysis of go/no-go task performance. Journal of Abnormal
Psychology, 123(2), 429–439. https:// doi. org/ 10. 1037/ a0036 295
Xu, M., Jiang, W., Du, Y., Li, Y., & Fan, J. (2017). Executive function
features in drug-naive children with oppositional deant disorder.
Shanghai Archives of Psychiatry, 29(4), 228–236. https:// doi. org/
10. 11919/j. issn. 1002- 0829. 216104
Zelazo, P. D., & Carlson, S. M. (2012). Hot and cool executive function
in childhood and adolescence: Development and plasticity. Child
Development Perspectives, 6(4), 354–360. https:// doi. org/ 10.
1111/j. 1750- 8606. 2012. 00246.x
Zelazo, P. D., Qu, L., & Kesek, A. C. (2010). Hot executive function:
Emotion and the development of cognitive control. In S. D.
Calkins & M. A. Bell (Eds.), Child development at the intersection
of emotion and cognition (1st ed., pp. 97–111). Washington, DC:
American Psychological Association.
Zhu, Y., Jiang, X., & Ji, W. (2018). The mechanism of cortico-
striato-thalamo-cortical neurocircuitry in response inhibition
and emotional responding in attention deficit hyperactivity
disorder with comorbid disruptive behavior disorder.
Neuroscience Bulletin, 34(3), 566–572. https:// doi. org/ 10. 1007/
s12264- 018- 0214-x
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional aliations.
Research on Child & Adolescent Psychopathology is a copyright of Springer, 2021. All
Rights Reserved.
Treatment Sequencing for Childhood ADHD: A Multiple-
Randomization Study of Adaptive Medication and Behavioral
Interventions
William E. Pelham Jr.1, Gregory A. Fabiano2, James G. Waxmonsky3, Andrew R. Greiner1,
Elizabeth M. Gnagy1, William E. Pelham III4, Stefany Coxe1, Jessica Verley2, Ira Bhatia2,
Katie Hart1, Kathryn Karch2, Evelien Konijnendijk2, Katy Tresco2, Inbal Nahum-Shani5, and
Susan A. Murphy5
1Florida International University
2State University of New York at Buffalo
3Pennsyvania State University
4Arizona State University
5University of Michigan
Abstract
Objective—Behavioral and pharmacological treatments for children with ADHD were
evaluated to address whether endpoint outcomes are better depending on which treatment is
initiated first, and, in case of insufficient response to initial treatment, whether increasing dose of
initial treatment or adding the other treatment modality is superior.
Methods—Children with ADHD (ages 5–12, N = 146, 76% male) were treated for one school
year. Children were randomized to initiate treatment with low doses of either (a) behavioral parent
training (8 group sessions) and brief teacher consultation to establish a Daily Report Card or (b)
extended-release methylphenidate (equivalent to .15 mg/kg/dose bid). After 8 weeks or at later
monthly intervals as necessary, insufficient responders were rerandomized to secondary
interventions that either increased the dose/intensity of the initial treatment or added the other
treatment modality, with adaptive adjustments monthly as needed to these secondary treatments.
Results—The group beginning with behavioral treatment displayed significantly lower rates of
observed classroom rule violations (the primary outcome) and parent/teacher ratings of
oppositional behavior at study endpoint and tended to have fewer out-of-class disciplinary events.
Further, adding medication secondary to initial behavior modification resulted in better outcomes
on the primary outcomes and other measures than adding behavior modification to initial
medication. Normalization rates on teacher and parent ratings were generally high. Parents who
began treatment with behavioral parent training had substantially better attendance than those
assigned to receive training following
medication.
Correspondence should be sent to: William E. Pelham, Jr., Professor of Psychology, Florida International University, 11200 SW 8th St
AHC1 146, Miami, FL 33199, 305-348-3002; fax 305-348-3646.
HHS Public Access
Author manuscript
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July
01.
Published in final edited form as:
J Clin Child Adolesc Psychol. 2016 ; 45(4): 396–415. doi:10.1080/15374416.2015.1105138.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Conclusions—Beginning treatment with behavioral intervention produced better outcomes
overall than beginning treatment with medication.
Keywords
Behavioral treatment; pharmacological treatment; ADHD
It is well established that evidence-based treatment for attention-deficit/hyperactivity
disorder (ADHD) includes medication with psychostimulants (Conners, 2002; Greenhill,
Pliszka, Dulcan, & the Work Group on Quality Issues, 2002) and behavioral interventions
(Pelham & Fabiano, 2008; Evans, Owens & Bunford, 2014; Fabiano et al., 2009). These two
modalities of treatment have been studied for decades, both separately and in combination.
Even so, disagreements remain among professionals regarding which treatment modality is
preferable, as well as how treatment for ADHD should begin. Some recommend beginning
medication immediately and supplementing with additional medication when necessary
(AACAP Work Group on Quality Issues, 2007). Others recommend beginning with
psychosocial treatments and adding medication if those treatments are insufficient (APA
Working Group on Psychoactive Medications for Children and Adolescents, 2006). Others
recommend starting with both treatments simultaneously (http://www.chadd.org). Most
recently, the American Academy of Pediatrics recommended each of the above strategies for
different ages of children (Subcommittee on Attention-Deficit/Hyperactivity Disorder,
Steering Committee on Quality Improvement and Management, 2011). However, the
research base upon which these recommendations have been made is scant and limited in
important ways (see for example Fabiano, Schatz, Aloe, Chacko, & Chronis-Tuscano, 2015).
In contrast to the hundreds of studies evaluating stimulants and behavioral interventions
separately, only a handful of randomized controlled trials (RCT) have compared medication,
behavioral treatment, and their combination, and each of these trials has limitations. A
common feature in the existing studies is that they have used fixed doses—typically
relatively high doses—of each treatment. For example, the largest and best-known RCT of
comparative treatments for ADHD is the MTA (MTA Cooperative Group, 1999a), which
used “optimal” dosing of medication (e.g., medication at school, evenings, and weekends)
compared with a package of intensive behavioral treatments (parent training, summer
treatment program, extensive teacher consultation, a classroom aide in school), and a
combined condition that added the two high-dose treatments and began them
simultaneously. The high-dose behavioral treatment was complex and costly, whereas the
high-dose medication treatment had adverse effects on growth. The results of the MTA vary
considerably based on the measure, individual differences, setting, timing of assessments,
length of follow-up, and interpretation (e.g., MTA Cooperative Group, 1999a, 1999b, 2004;
Molina et al., 2009; Pelham, 1999; Pelham et al., 2000; Owens et al., 2003; Swanson et al.,
2007), suggesting that additional finely-tuned investigations with different doses and
sequences of treatments are necessary to clarify relative effects of the two major, evidence-
based treatment modalities.
More recent research has used both within-subject and RCT designs to evaluate multiple
doses of medication in different combinations with varying doses of behavioral treatments
Pelham et al. Page 2
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
(Fabiano et al. 2007; Pelham et al., 2005; Pelham et al., 2014; Pelham et al., under review).
These studies have consistently found that intensive behavior modification produces acute
effects similar to relatively high doses of medication, but that low doses of both treatments
also maximize response in some but not all children. Further, these studies show that
combining low-dose medication with low-intensity behavioral interventions produces
equivalent effects to those of high-dose/high-intensity unimodal treatments for the majority
of children but with lower side effects, high parental satisfaction, and less complex
behavioral interventions. Side effects of stimulants increase with escalating dose and
duration of exposure (Barkley et al. 1990; Pelham et al, 1999; Stein et al, 2003, Swanson et
al., 2007). Therefore, adding behavioral interventions that reduce medication dose should
improve the tolerability of medication treatments. These studies have provided much-needed
information regarding the relative effects of different doses of medication and behavior
modification. However, they were implemented in an analogue summer treatment program
setting and thus do not directly address whether low doses of either modality or their
combination would be sufficient for many children in
community settings.
Moreover, no studies in the literature have systematically varied and compared the sequence
in which the two evidence-based modalities for ADHD are implemented. Medication is the
most commonly employed intervention and often the only intervention used in practice
(Epstein, et al., 2014; Visser et al. 2014) even in young children where professional
guidelines recommend starting with behavioral treatments (Subcommittee on Attention-
Deficit Hyperactivity Disorder, 2011). Psychiatric guidelines endorse optimizing dose at
home and school and using multiple medications prior to adding behavioral treatments
(AACAP Work Group on Quality Issues, 2007). When medication is implemented at this
high intensity level, there is less need for behavioral interventions, so the opportunity does
not exist to discover whether some or most children would do well with behavioral
interventions alone. For example, 75% of the individuals in the MTA behavioral treatment
group remained without medication during the year of treatment, and, for the majority of
those, for years afterward (MTA Cooperative Group, 1999a, 2004, Molina et al., 2009). This
implies that many children might not need medication if behavioral treatments were
employed first. Further, the majority of children in the medication management group
needed additional treatment during the 14-month treatment period, but only medication
could be used in this condition, and maintaining the initial medication effect required a 25%
increase in dose during the year of treatment (Vitiello et al., 2001). In the combined
treatment group, an adjustment to the classroom intervention—most often the Daily Report
Card (DRC)—had to be made before medication dose could be increased, and that
procedure reduced the need for increased doses of medication (Vitiello et al., 2001). The
simultaneous introduction of conditions in the combined treatment group in the MTA means
that it is not possible to evaluate whether a behavioral intervention employed before
medication would have prevented the need for medication or reduced the dosage needed.
Thus, a significant limitation of existing ADHD treatment studies is that questions regarding
sequencing, dosing, and combining treatments in natural settings have not been
systematically explored. In contrast to this body of research, treatment decisions in practice
are ongoing, based on the child’s impairment and response to intervention, and typically
provided initially at low “doses” that are escalated only if necessary. There are two crucial
Pelham et al. Page 3
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
decision points in treating a child with ADHD: (1) which treatment should be implemented
first? and (2) what should be done if the child does not respond adequately to that initial
treatment? For example, if a child begins treatment with medication and fails to respond,
there are two possibilities – increase the medication dose or add behavioral treatment. These
decision points have many implications with regard to tolerability/side effects, treatment
cost, and treatment efficacy, yet no studies have systematically evaluated such treatment-
sequencing questions for ADHD.
Adaptive treatment strategies have been gaining recognition as a strategy for preventive
interventions and management of chronic disorders (Murphy, 2005; Collins, Murphy, &
Bierman, 2004). In an adaptive approach, different dosages of treatment are provided
differentially to individuals across time in response to decision rules that are based on
individual characteristics. The major advantage of adaptive treatment designs is that they
mimic what happens in typical practice where treatments are often modified or enhanced,
but they retain controlled procedures, dosages, and rules to ensure replicability. Adaptive
approaches have previously been used with comprehensive services of the type that are used
for children with ADHD (for examples see Conduct Problems Prevention Research Group,
1999a, 1999b). Thus, in the present investigation, we employed a research design that has
been recommended for developing and comparing adaptive strategies, a sequential multiple
assignment randomized trial (SMART; Murphy, 2005; Lavori & Dawson, 2000). In such
trials, individuals are randomized at multiple decision points to produce each treatment
strategy, combinations of which can be analyzed because they have been assigned by
randomization.
The current study was undertaken to address the limitations in the existing treatment
literature for children with ADHD with regard to treatment decisions and sequencing. A
SMART design was used to compare the results of various treatment decisions that included
behavioral and/or pharmacological interventions and their combination that can be widely
applied in clinical practice. Starting with low doses, treatments were conducted over an
entire school year in children’s school and home settings and adapted monthly within setting
based on response and need for additional intervention. End-of-study outcomes were
measured on objective classroom observations of behavior and parent/teacher ratings to
determine the relative benefits of the treatments and their sequences.
Within this design we were able to examine three important clinical questions/aims. First
(Aim 1): does it produce better outcomes on endpoint objective classroom measures and
parent/teacher ratings to initiate treatment with a low dose of (a) pharmacological
intervention with a stimulant drug or (b) behavioral intervention (group parent training and a
DRC at school)? Second (Aim 2), what is the most effective treatment protocol, or pattern of
initial treatment and conditional secondary/adaptive treatment (e.g., BM: behavioral
followed by medication in the event of insufficient response) among the four that we
employed (BM, Behavioral-Behavioral (BB), Medication-Behavioral (MB), and
Medication-Medication (MM)? Third (Aim 3), in the event of insufficient response to one of
the initial treatments, are endpoint results improved more by increasing the dose of that
modality (e.g., adding secondary/adaptive B to initial B (B then B) when necessary) or
Pelham et al. Page 4
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
adding treatment with the other modality (e.g., adding secondary/adaptive M to initial B (B
then M)?
Methods
Participants
One hundred, fifty-two children with ADHD, between the ages of 5 and 12, participated.
Participants were recruited in three cohorts of approximately 50 each via radio
advertisement; direct mail; and referrals from schools, physicians and mental health
providers. Recruitment occurred during the spring and summer of 2006, 2007, and 2008,
with treatment commencing in September of each year and continuing throughout the school
year.
Exclusionary criteria included: (1) Full Scale IQ below 70; (2) history of seizures or other
neurological problems and/or medication to prevent seizures; (3) history of other medical
problems for which psychostimulant treatment may involve considerable risk; (4) childhood
history or concurrent diagnosis of pervasive developmental disorder, schizophrenia or other
psychotic disorders, sexual disorder, organic mental disorder, or eating disorder; (5) lack of
functional impairment; and (6) placement in special education classrooms.
After screening and informed consent, parents and teachers completed a number of
instruments to determine diagnosis and study eligibility. To determine ADHD diagnosis,
parents and teachers completed the Disruptive Behavior Disorders (DBD) Rating Scale
(Pelham, Gnagy, Greenslade, & Milich, 1992). The DBD RS is a list of the DSM symptoms
of ADHD, oppositional-defiant disorder (ODD) and conduct disorder (CD), updated for
DSM-IV, and rated as not at all, just a little, pretty much, or very much. In addition, parents
completed a semi-structured DBD interview consisting of DSM-IV symptoms of ADHD,
ODD, and CD with supplemental situational probes (available from the first author). Parents
and teachers completed the Impairment Rating Scale (IRS: Fabiano et al., 2006), which asks
parents and teachers to evaluate on a six point Likert scale the degree to which a child is like
a typical child and needs no treatment or has extreme problems that definitely require
treatment or special services in five areas of function—relationship with parents/teachers,
relationships with peers/siblings, academic progress, general classroom/family functioning,
and overall functioning. Two clinicians independently reviewed all screening instruments
and made diagnoses based on the DSM-IV rules, counting a symptom as present when
endorsed by either teacher or parent (pretty much or very much on the DBD or parent
interview). Impairment also had to be present in any domain, as indicated by cutpoints on
the Impairment Rating Scale (IRS; Fabiano et al., 2006). In case of disagreement, a third
clinician reviewed the file to determine final diagnosis. Eighty percent of the children met
criteria for ADHD-Combined Type, with 15% Predominately Inattentive and 5%
Predominately Hyperactive/Impulsive. Comorbid rates of ODD and CD are shown in Table
1, along with demographic and descriptive information. None of the ADHD diagnoses and
only 2% of the ODD and CD cases required a third reviewer to confirm diagnosis.
The sample size was determined using data from our previous study in a controlled setting
(Fabiano et al., 2007; Pelham et al., 2014) to estimate effect sizes for the first-stage
Pelham et al. Page 5
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
treatments used in this study. These calculations determined that a sample size of 150 would
result in at least 80% power for testing first-stage differences of at least 0.5 standard
deviations when testing at a 0.05 level of significance. Recruitment and participant flow are
illustrated in Figure 1. Six participants withdrew prior to the end of the study; 146 children
completed the study assessments (96%). Three families withdrew because they did not wish
to use medication and three withdrew because teachers refused participation upon initial
contact after the family had been randomized. In the context of this multiple-randomization
design, early withdrawal results in missing data on the group membership variable (i.e.,
responder versus non-responder). Although there are methods that can address this particular
challenge (e.g., Shortreed, Laber, Scott-Stroup, Pineau, & Murphy, 2014), the subsequent
analyses utilized only the 146 completers despite the use of multiple imputation to address
missing data.
Design
Figure 2 illustrates the study design. Participants were randomly assigned to one of two
initial treatments that were initiated at the beginning of the school year: low-dose medication
for school hours only—Medication First (MedFirst) —or low-intensity clinical behavioral
intervention consisting of weekly behavioral parent training groups (BPT) and a school
consultation to establish a Daily Report Card (DRC: Jacob & Pelham, 2000; available at
http://ccf.fiu.edu; Volpe & Fabiano, 2013)—Behavior First (BehFirst). Eight weeks of
treatment were then provided to allow for sufficient time to implement behavioral treatments
and medication and to measure their impact, after which each participant’s response was
measured according to the procedures described below. If a child experienced continued
impairment in the school and/or home setting—that is, insufficient response to the initial
treatment—then a second randomization occurred. At this point, one of two treatment
strategies was employed in the setting(s) where impairment was present: (1) increase the
dose/intensity of the initial treatment or (2) add the other treatment for a combined treatment
modality. Children who responded to the initial treatment condition were maintained on that
condition and monitored monthly; if their performance deteriorated at any time during the
school year, then the second treatment randomization occurred at that time. Children’s
progress continued to be monitored and their secondary treatment condition was tailored
adaptively (initial treatment was not tailored). For example, a child who began treatment
with a 10-mg dose of medication and was re-randomized to receive behavioral treatment
stayed on the initial 10-mg dose for the remainder of the school year, and subsequent
changes were made only to the adaptive behavioral part of the treatment. Treatments,
evaluations of response, and treatment adjustments were made independently for the home
and school settings, which afforded independent evaluations of need for treatments,
adherence, uptake, and effectiveness at home and at school.
Assessing need for additional treatment—Each month, parents and teachers
completed ratings on a study-specific version of the IRS. The IRS was modified to ask
whether, given the treatment currently in place, the child needed additional treatment, with
responses ranging from 1 (definitely not) to 5 (definitely yes). If a rater responded probably
yes or definitely yes in any domain, a study staff member called the rater to ask follow-up
questions about the child’s impairment to ascertain whether the rating indicated true need for
Pelham et al. Page 6
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
additional services, and to ensure that the impairment could be addressed with the available
treatments (e.g., clinicians ruled out that a significant life event may have triggered a
temporary increase in problem behavior or that comorbid learning problems may have
accounted for impairment in academic progress).
As an objective measure of response to intervention in the school setting, teachers also kept
records from an individualized target behavior evaluation (ITBE; Pelham, Fabiano &
Massetti, 2005). The ITBE is sensitive to treatment effects, can be implemented by general
education classroom teachers, and is individualized to children’s areas of impairment
(Fabiano, Vujnovic, Naylor, Pariseau, & Robins, 2009; Fabiano et al., 2010). During the first
few weeks of school, a study case manager met with each child’s teacher to establish target
behaviors (e.g., work completion, complying with teacher directions, behavior toward peers)
and criteria for what the teacher considered success on that target behavior. ITBE goal
attainment percentages were computed across class periods each day, and weekly averages
were calculated for evaluation. ITBE results were not shared with children or parents for
children in the MedFirst group. For children in the BehFirst and adaptive behavioral groups,
the ITBE doubled as a DRC and was sent home to parents, who provided contingent
consequences at home.
At the 8-week point and monthly thereafter, the study team met to discuss each case. If
monthly IRS ratings indicated impairment, the study team ensured that the impairment was
related to an appropriate target of study treatments. Two clinicians who were not directly
involved in the child’s treatment and were unaware of the initial treatment condition were
required to agree that additional treatment was necessary based on the teacher or parent IRS
before the child could be rerandomized. In the school setting, ITBE performance was
simultaneously evaluated; if weekly averages consistently fell below 75%, and need for
additional treatment was also indicated on the IRS, then additional treatment was
considered. Finally, if a child was in immediate danger of class failure or school suspension,
these factors were taken into account in treatment decisions.
For the children who were rerandomized, monthly treatment decisions were made regarding
additional dose increases or adjustments to the behavioral treatment. These decisions were
made using the same criteria as for initial response. Treatment recommendations were
tailored to specific domains of impairment as described below. Parents were able to decline
treatment recommendations for medication or additional behavioral services, but
recommendations were reoffered monthly if indicated. All treatment recommendations, the
reasons for them, and records of treatments received were documented.
Treatment Descriptions
Table 2 lists the components of the low and high dose medication and behavioral treatments.
Children were initially randomized to a dose of behavioral or medication treatment, with
additional treatment added, if indicated, based on a second randomization (see Figure 2).
Initial Treatments—For children assigned to the BehFirst condition, parents received an
8-session, group parent training program using the Community Parent Education Program
which has been extensively used with ADHD children (COPE; Cunningham, Bremner, &
Pelham et al. Page 7
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Secord-Gilbert, 1998); children participated in concurrent group social skills training
sessions, modified after a recreational period in the Summer Treatment Program (STP;
Pelham et al., 2010). Prior to the first group parenting session, parents received an individual
session to establish a home reward system for the DRC. The case manager also conducted
three brief consultation visits with the child’s teacher regarding standard classroom
management strategies. This included an initial review of the teacher’s classroom
management practices, discussion of basic classroom management, including praising
appropriate behavior, planned ignoring, and appropriate commands, as well as procedures
related to implementing a DRC. DRCs were sent home each day and parents provided daily
and weekly rewards for good performance at school. Following the initial 8-week treatment
period, monthly parent-training booster sessions with a focus on maintenance and problem-
solving were offered for the remainder of the school year. The case manager also
communicated with the teacher each month regarding adjustments to the DRC and the basic
classroom management interventions that were in place.
For children assigned to the MedFirst condition, a dose equivalent to 0.15 mg/kg/dose b.i.d.
of immediate release methylphenidate was calculated. In order to separate home and school
settings for assessment and interventions, an 8-hour extended release preparation of
methylphenidate (MPH) was used for the school setting only. For most children (92%), this
was 10 mg per day of the extended release MPH preparation; for the remainder, their initial
dose was 20 mg daily. School doses were administered by parents in the morning prior to
school, and home meds were administered after school and on weekend mornings. The 0.15
mg/kg dose was selected based on data from controlled studies (Fabiano et al. 2007, Pelham
et al., 2005; Pelham et al., 2014) showing significant effects over placebo that are similar to
a low intensity behavioral intervention with very good tolerability. Side effects were
monitored weekly for the first two weeks of medication administration and monthly
thereafter; spontaneous reports of side effects were also collected. Any time a child
experienced moderate or severe side effects, the study physician made dosing adjustments if
necessary. The case manager also adjusted the ITBE for children in this group as needed
monthly based on teacher report.
Secondary (Adaptive) Treatments—For children who began with behavioral
treatment and were rerandomized to receive secondary/adaptive behavioral treatment (B-
then-B), more intensive standard behavior management procedures were implemented first
to address individualized areas of impairment. At school, these included school-based
rewards for DRC performance, classwide reward contingency systems, intensive classroom-
based contingency programs administered by the teacher or a paraprofessional, and time-out
procedures. Home-based DRCs and individual parent-training sessions were introduced in
the home setting. Other interventions were then added to address specific areas of child
impairment (See Table 2).
For MedFirst children who began with medication and then were rerandomized to
secondary/adaptive behavioral intervention (M-then-B), the standard initial behavioral
treatments were implemented first (i.e., group parent training, DRC consultation). After
eight weeks, the additional services described above were added according to the child’s
continued impairments and need for tailored behavioral treatments.
Pelham et al. Page 8
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
For children who began with medication treatment and were rerandomized to receive
secondary/adaptive medication (M-then-M), adjustments were made in two ways. First, the
morning dose of the extended release MPH preparation given on school days could be
increased if problems continued at school. Second, an after-school dose of immediate release
MPH could be added to the child’s regimen if home behavior or homework completion were
impaired (cf. Greenhill et al., 1996). In addition, MPH could be added for weekends. Parents
also had the option of switching to a 12-hour formulation if the criteria for additional
treatment were met in both settings.
For children who began with behavioral treatment and were rerandomized to receive
secondary/adaptive medication (B-then-M), medication could be added as above for school,
home, or both settings. Performance was evaluated monthly and adjustments were made
taking into account impairment level and side effects.
Primary Dependent Measure
Classroom rule violations—As it is commonly regarded as the gold standard in
assessments of treatment outcome for ADHD children in school settings, we used objective
observation of student behavior in the classroom context as our primary dependent measure
(Fabiano et al, 2009; Pelham, Fabiano, & Massetti, 2005). Every 4–6 weeks, independent
observers visited the children’s classrooms and conducted 40-min. direct observations
during academic tasks. Observers used the Student Behavior/Teacher Response code
(available from first author), which includes observations of children’s rule-breaking
behaviors (i.e., disrespect toward others, noncompliance with teacher requests, disrupting
others, leaving seat without permission, inappropriate use of materials, speaking out without
permission, and off-task behavior) and the teacher’s response to those behaviors (e.g.,
ignoring, providing a reprimand, providing a consequence; Vujnovic et al., 2014). Child
behaviors were coded independently of teacher responses and were coded even if the teacher
did not observe the behavior. Observers watched the entire class and coded behaviors
exhibited by the target child and classmates. Classmates were observed anonymously and
were not identified to the observer. The average number of behaviors exhibited by
classmates was computed to produce a classroom comparison rate used as a covariate in
analyses. The final observation of the school year was used in endpoint analyses because it
corresponded with the time interval during which parent and teacher endpoint ratings were
collected
In order to enhance reliability, observers were required to memorize operational definitions
of behavior categories and completed a training session consisting of role-plays, practice
observations, and classroom observations with an experienced observer. For 21% of the
classroom observations, a second trained observer accompanied the primary observer and
conducted an independent reliability observation of the same classroom. Reliability of the
observations was high, with a correlation of 0.91 (p<0.01), and a mean difference of 2.3 (SD = 2.8; range = 0–17) for the total classroom rule violations. These figures are consistent with
those from previous studies using the same observational code (e.g., Fabiano et al., 2010).
Pelham et al. Page 9
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Secondary Dependent Measures
Number of out-of-class disciplinary events—Teachers kept daily records of out-
of-class disciplinary events (e.g., being sent to the principal’s office). The number of
reported events was summed over the length of the school year for subsequent analysis.
Parent and teacher ratings—At endpoint, parents and teachers completed the DBD
RS and the Social Skills Rating System (SSRS; Gresham & Elliott, 1989). These measures
have been widely used in studies of ADHD and have published psychometric information.
Tracking of Treatment Fidelity
Attendance records were kept for all treatment sessions, and clinicians recorded all meetings
and contacts with teachers and parents. Medication dispensing records were kept. Parents
returned all unused pills at each medication visit, and pill counts were conducted to
determine the number of pills used.
To ensure fidelity with the behavioral treatments, all treatment components were manualized
and procedural checklists were developed for all parent and teacher sessions. Clinicians met
weekly with supervisors to review records of their sessions, and supervisors provided
feedback as necessary. At each classroom observation, the observer recorded whether or not
the teacher implemented the prescribed behavioral management procedures during the
observation period.
Missing Data Handling
Missing values were minimal across all study variables and all participants. Outcomes
ranged from 0 to 14% missing: classroom rules violations (98% complete), out-of-class
disciplines (97%), teacher DBD rating (99%), teacher SSRS rating (99%), parent DBD
rating (90%), parent SSRS rating (86%), and final medication doses (100%). At the
participant level, 125 of 146 (86%) participants had complete data for all of the analyzed
outcomes. We used multiple imputation to ensure unbiased estimates, assuming the data to
be Missing at Random (MAR). Here MAR is a plausible assumption given the inclusion of a
large number of covariates, including baseline measures of outcome variables; measures’
values at earlier waves are typically the best predictors of missing values at later waves in a
longitudinal design.
Imputation—In order to accommodate the non-normal distributions of many relevant
variables, we implemented a chained equations approach in R 3.1.3 (R Core Team, 2015)
using the mice package (v2.22; van Buuren & Groothuis-Oudshoorn, 2011) extended by the
countimp package for imputing count variables (v1.0; Kleinke & Reinecke, 2013). As
methodologists recommend an inclusive strategy (Collins, Schafer, & Kam, 2001), the
imputation model included approximately 50 variables: all the variables in the subsequent
analyses, all the sample characteristics listed in Table 1, and baseline measures of outcomes
wherever available. Distributions of all imputed variables were inspected and each was
modeled using normal, predictive mean matching, negative binomial, logistic, and
multinomial regressions, as indicated. Due to the large number of items and their high
correlations (i.e., multiple items from the same measure), imputation occurred at the level of
Pelham et al. Page 10
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
the scale rather than at the level of the item. One hundred imputed data sets were created,
following recent recommendations that using larger number of imputations can minimize
simulation error (White, Royston, & Wood, 2011).
Analysis and pooling—All subsequent analyses were conducted in SAS 9.3. Analysis
was completed separately on each of the 100 imputations according to the procedures
described in subsequent sections. SAS 9.3 PROC MIANALYZE was used to combine
estimates across imputations; all reported estimates represent these combined (or pooled)
estimates.
Analytic Plan
Our analyses largely parallel those described by Nahum-Shani and colleagues (2012); we
direct readers to that article for more details about SMART design analyses. In the present
study, the analysis of treatment outcome data included a series of comparisons to test
different treatment decisions. Each comparison is described below.
Main effect of initial treatment assignment on endpoint outcomes (Aim 1)—
End-of-treatment outcomes of those that started with medication (MedFirst group) and those
that started with behavioral treatment (BehFirst group) were compared using regressions
with group membership as a predictor in order to examine whether the initial treatment
modality impacted outcome. In addition, survival analyses were conducted to determine
whether the groups differed in the need for additional treatment and the length of time
before children needed additional treatment.
Pairwise comparisons among SMART-embedded treatment protocols on
endpoint outcomes (Aim 2)—Second, outcomes were compared across each of the four
treatment protocols naturally embedded in the SMART design—BB, BM, MB, and MM.
The first letter denotes that protocol’s initial treatment (first-stage treatment in Nahum-Shani
et al, 2012) and the second letter denotes that protocol’s secondary/adaptive treatment
(second stage treatment in Nahum-Shani et al, 2012), to be implemented in the event of
insufficient response to the initial treatment. For example, the BM protocol entailed starting
the participant with behavioral treatment and then adding medication if and only if there was
insufficient response. It is important to note that the protocols do not reflect the actual
treatment received, but rather the set of rules followed to assign treatment at both stages. For
example, a child who responded to Behavior First and is therefore never rerandomized to
receive secondary/adaptive treatment is included in analyses of the BM protocol even though
he did not receive medication. This idea of being consistent with a particular embedded
protocol is a subtle but important aspect of the SMART design that is discussed in detail
elsewhere (Nahum-Shani et al., 2012). In the present analyses, we used an effects coding
scheme and generalized estimating equations to achieve all the pairwise comparisons of
protocols in a single model using SAS PROC GENMOD with robust standard errors, as
described in the appendices of Nahum-Shani et al. (2012). We also gave weights of 2 to the
responders to first-stage treatment and weights of 4 to the insufficient responders in order to
account for the systematic undersampling of the latter in each protocol due to the second re-
randomization (Nahum-Shani et al., 2012).
Pelham et al. Page 11
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Comparison of endpoint outcomes for secondary/adaptive treatments given
insufficient response to initial treatment (Aim 3)—Third, supplemental comparisons
were performed within each of the initial treatment arms to determine whether it is better to
augment (i.e., increase the dose of) that treatment or add the other treatment, given
insufficient response to an initial intervention. Thus, responders to the initial treatments were
excluded from these comparisons. These analyses consisted of regressions with group
membership as a predictor that compared (1) B-then-B with B-then-M and (2) M-then-M
with M-then-B.
Normalization rates—Finally, we used the procedure reported in Swanson et al. (2001)
to evaluate normalization of functioning on teacher and parent ratings at the study endpoint.
A score of 1.0 or lower on an aggregate of ADHD and ODD items from the DBD Rating
Scale was used to define normalization. Teacher and parent reports were examined
separately due to the separation of interventions across settings.
Count outcomes—Two dependent variables were counts: observed classroom rule
violations and number of out-of-class disciplinary events. Count outcomes often violate the
assumptions of linear (OLS) regression, so we adapted the SMART analysis procedure to
incorporate negative binomial regression, a robust approach to modeling count outcome
variables (Coxe, West, & Aiken, 2009). Negative binomial regression is related to the more
well-known Poisson regression, but relaxes some assumptions of Poisson regression that are
typically not met (i.e., equidispersion). As with the continuous outcomes, SAS PROC
GENMOD was used for these analyses, with the addition of the negative binomial modeling
and incorporating the OFFSET option to adjust for individual differences in the length of
observation intervals. In the observed classroom rule violations analyses, average peer rule
violations per hour was included as a covariate to control for the general level of disruptive
behavior in each classroom.
Results
Need for Additional
Treatment
In the school setting, 67% of the children who began treatment with behavioral interventions
required additional treatment by the end of the school year compared with 47% of the
children who began the school year receiving a low dose of medication (odds ratio or
OR=2.23). Survival analyses indicated a significant group difference; Breslow χ2=7.4, p < .
01.
In the home setting, there was no difference in rate of rerandomization for BehFirst (82%)
and MedFirst (88%) groups; OR=0.63. Almost all children met criteria for additional
treatment in the home setting regardless of initial treatment.
Endpoint Classroom Observations
Tables 3–6 display the results of analyses for classroom rules violations as well as
subsequent outcomes. Comparisons of initial treatment strategy (BehFirst vs. MedFirst)
revealed a significant difference on classroom rule violations, as illustrated in Figure 3.
Pelham et al. Page 12
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Children who began treatment with behavior management exhibited significantly fewer rule
violations per hour than children who received MedFirst (incidence rate ratio or IRR=0.66,
p<.01). Pairwise comparisons of the four treatment protocols revealed several significant
differences (Figure 3). The BB protocol resulted in fewer rule violations than the BM
protocol (IRR=0.78, p=.054), the MM protocol (IRR=0.56, p<.01), and the MB protocol
(IRR=0.50, p<.001). In addition, the BM protocol resulted in fewer rules violations than the
MB protocol (IRR=0.65, p<.01). For insufficient responders to first-stage behavioral
treatment, increasing the dose with second-stage behavioral treatment resulted in
significantly fewer violations than did adding medication (IRR=0.71, p<.05). For insufficient
responders to first-stage medication treatment, there were no significant differences between
second-stage treatments.
Out-of-Class Disciplinary Events
Comparisons of initial treatment strategy revealed a trend wherein the BehFirst group
displayed fewer out-of-class disciplinary events than the MedFirst group (IRR=0.52, p<.10,
Figure 3). Pairwise comparisons of the four treatment protocols indicated that (Figure 3): the
BM protocol resulted in significantly fewer events than the MB protocol (IRR= 0.16, p<.
001) and the BB protocol (IRR=0.34, p<.05), and the MM protocol resulted in significantly
fewer events than the MB protocol (IRR=0.34, p<.10). For insufficient responders to initial
behavioral treatment, adding medication trended toward resulting in significantly fewer
events than increasing the dose of behavioral treatment (IRR=0.30, p<.10). For insufficient
responders to first-stage medication treatment, increasing the dose with medication
treatment trended toward resulting in fewer events than did adding behavioral intervention
(IRR=0.27, p<.10).
Teacher Ratings
On teacher DBD ratings, no significant differences emerged for ADHD symptoms. For
ratings of oppositional/defiant behavior, the pairwise comparisons of the four treatment
protocols indicated a near significant advantage of the BM protocol over the MB protocol
(d=0.40, p=.06). The supplemental comparisons indicated that for insufficient responders to
first-stage medication, increasing the dose with second-stage medication trended toward
resulting in lower ratings of oppositional/defiant behavior than did adding behavioral
(d=0.61, p<.10).
For Total Social Skills score of the teacher SSRS, there was a trend toward advantage of the
BM protocol over the MB protocol (d=0.35, p<.10). Other comparisons were nonsignificant.
With regard to normalization of combined ADHD/ODD teacher ratings at endpoint, similar
numbers of the children assigned to MedFirst or BehFirst had mean DBD ratings of 1.0 or
less—69% and 78% respectively. Eighty-four percent of those who responded to first-stage
medication treatment met the normalization criterion, as did 92% of those who responded to
first-stage behavioral treatment. For those needing additional treatment, 63% of the M-then-
M group was normalized, compared to 61% of the B-then-B group, and 38% of the M-then-
B group compared to 80% of the B-then-M group.
Pelham et al. Page 13
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Parent Ratings
As with teachers, there were no significant differences on ADHD ratings in any
comparisons. For ratings of oppositional/defiant behavior, pairwise comparisons of the four
treatment protocols revealed a significant advantage of the BM protocol over the MB
protocol (d=0.56, p<.05) and BB protocol (d=0.38, p<.10), as well as a trend advantage of
the MM protocol over the MB protocol (d=0.40, p<.10). The supplemental comparisons
indicated that for insufficient responders to first-stage behavioral, adding second-stage
medication trended toward resulting in lower ratings of oppositional/defiant behavior than
did increasing the intensity of behavioral treatment (d=0.45, p<.10). Likewise, for
insufficient responders to first-stage medication, increasing the dose with second-stage
medication trended toward resulting in lower ratings of oppositional/defiant behavior than
did adding behavioral (d=0.46, p<.10). There were no significant differences in any
comparisons of the Total Social Skills score of the parent SSRS.
With regard to normalization of ADHD/ODD parent ratings at endpoint, 31% of the
MedFirst and 39% of the BehFirst, groups met criteria for normalization. Two-thirds of
those who responded to first-stage medication treatment met the normalization criterion, as
did 54% of those who responded to first-stage behavioral treatment. For those needing
additional treatment, 34% of the M-then-M group was normalized, compared to 30% of the
B-then-B group, and 18% of the M-then-B group compared to 40% of the B-then-M group.
Treatment Received
For those who began with BehFirst, 3% of the families declined parent training. Remaining
parents attended an average of 6 of the 8 group sessions (median=7, mode=8), 69% attended
an adequate dose of parent training (cf. MTA Cooperative Group, 1999), and 31% attended
at least one booster session after the initial parent training (see Figure 4). All scheduled
teacher meetings were completed, and DRCs were established for all but one child in the
BehFirst condition. For children in B-then-M, 13 parents (21%) declined the initiation of
medication.
At the initial 8-week assessment point, 9% of the MedFirst families had declined
medication. Of those who accepted medication, they were medicated on 97% of their school
days. For those in MedFirst rerandomized to the M-then-B group, 60% of these parents did
not attend any of the assigned group parent training sessions (mean=1.9, median=0,
mode=0); only 11% of these families received an adequate dose of parent training, and only
11% attended at least one booster session (Figure 4). At school, all required teacher
meetings were completed and school DRCs were established.
In the adaptive behavioral treatment arms (either M-then-B or B-then-B), 11 children
attended Saturday Treatment Program sessions, 3 received extra academic tutoring, and 13
received additional intensive interventions at school (e.g., the good behavior game initiated
as a classwide intervention) administered by the teacher or a paraprofessional as part of the
adaptive behavioral treatment condition. The remainder received either additional standard
teacher consultations to establish higher-intensity teacher-delivered consequences such as
school-based rewards and class-wide contingencies or individual parent sessions to improve
Pelham et al. Page 14
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
parenting skills or establish more intensive parent-delivered interventions at home. Eleven
families assigned to behavioral treatment took medication outside of the protocol. Five of
these families used the medication for only 1–2 months before stopping the medication.
To determine whether beginning treatment with behavioral intervention would decrease the
dose of medication required for a child treated with medication in school, school-day dosing
in mg/kg/dose equivalent was compared for the two groups involving adaptive medication:
B-then-M and M-then-M at endpoint. At the end of the school year, 24% of the B-then-M
group was unmedicated at school compared with 5% of the M-then-M group (that is, parents
either did not start or elected to stop medication). Of those who were medicated, children in
B-then-M were taking significantly lower doses at school (M=0.21 mg/kg/dose, SD=0.10)
than M-then-M (M=0.28, SD=0.14), F(1, 70)=4.26, p<.05. At home, 39% of the M-then-M
group and 35% of the B-then-M group were unmedicated (parents either did not start or
elected to stop medication). For those who were medicated at home, doses were not
significantly different: (B-then-M: M=0.22, SD=0.10; M-then-M: M=0.21, SD=0.12).
Discussion
This study addressed three key questions: first (Aim 1), does it produce better outcomes on
objective classroom measures and teacher and parent ratings to begin treatment with a low
dose of (a) medication treatment or (b) behavioral treatment? Second (Aim 2), what is the
most effective treatment protocol, or pattern of first-stage treatment and conditional second-
stage treatment among the four imbedded SMART treatment protocols? Third (Aim 3), in
the event of insufficient response to a specific initial treatment, is it more effective to
increase the dose of that modality or add treatment with the other modality? All groups were
functioning relatively well at endpoint, as was expected given that two effective treatments
were compared. However, there were important differences in outcomes, as a result of the
initial treatment assignment and the protocol followed. Our findings provide the following
answers to the three questions/Aims noted above as follows:
1. Beginning treatment with a low dose of behavior modification resulted in
significantly lower rates of observed classroom rule violations and a trend for
out-of-class disciplinary events relative to beginning with a low dose of
medication.
2. The best of the four specific treatment protocols began with behavioral
treatment and then added medication in the event of insufficient response
(BM). The worst protocol began treatment with medication and added
behavioral treatment in the event of insufficient response (MB). The BB and
MM protocols produced outcomes in between these two and were often
comparable, though BB was superior to MB and MM on the primary outcome
variable.
3. In the event of insufficient response to initial behavioral treatment, increasing
the intensity of behavioral treatment (B-then-B) was significantly superior on
the primary outcome (classroom rule violations); adding medication (B-then-
M) had nominal advantages on several other outcome variables, two of which
Pelham et al. Page 15
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
were trends. In the event of insufficient response to medication, increasing
dose of medication (M-then-M) was nominally superior to adding behavior
modification (M-then-B) on every measure with small to moderate effect sizes,
three of which were trends.
These results have clear implications for treatment for children with ADHD in mental
health, primary care, and school settings, and we discuss each in turn.
With regard to our first question, beginning school-based treatment with a low dose of
behavior modification (eight sessions of group parent training plus establishing a DRC at
school with home rewards) resulted in functioning in the school setting on key outcome
measures that was comparable to or better than beginning school-based treatment with a low
dose of stimulant medication. Notably, the low dose of behavior modification was a superior
starting strategy on the primary outcome measure, direct observations of classroom behavior
(66% as many rule violations), as well as the frequency of out-of-class discipline (54% as
many incidents). Although teacher ratings did not differentiate BehFirst from MedFirst,
ratings of oppositional behavior decreased by more than 50% from baseline in the BehFirst
group, and teachers rated 78% of children in the BehFirst group as normalized. For 33% of
the BehFirst children, the low dose of behavioral intervention was sufficient treatment in
school for the entire school year.
There were no differences between the groups in the numbers of children who needed
additional treatment at home, with more than 80% of both groups meeting criteria for
rerandomization (see discussion below). Similarly, there were no significant differences
between initial treatments on parent ratings of symptoms, oppositional behavior, or social
skills.
Interestingly, compared to the 33% who did not need additional school-based treatment in
BehFirst, nearly two-thirds more, 53% of children in the MedFirst initial assignment did
sufficiently well with the low dose of medication that they did not require additional
treatment. Further, a substantial portion of the children assigned to the BM embedded
protocol (24%) were not taking medication at endpoint—far more than the MB protocol. In
other words, although the combined treatment protocol (MB) within the MedFirst arm
contained more medicated children than did the combined treatment protocol (BM) within
the BehFirst arm, and although there were more initial responders in the MedFirst group
than in the BehFirst arm, MedFirst remained inferior to BehFirst as an initial treatment
condition.
These differences in the effects of the initial intervention may be linked to differences in
treatment uptake of parent training, as shown in Figure 4. Engagement in the parent training
groups and the booster sessions was dramatically reduced in the MedFirst families relative to
the BehFirst families. Indeed, most BehFirst parents attended the majority of BPT sessions
and received an adequate “dose” of BPT, while only a small minority of MedFirst families
who were assigned to BPT as a secondary/adaptive intervention attended BPT. In other
words, the provision of medication before the initiation of parent training was associated
with greatly reduced rates of engagement in parent training, and presumably worse
functioning at school and home. This finding parallels those reported in the STAR*D study
Pelham et al. Page 16
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
of antidepressants for adults with major depression: 71% of adults that were insufficient
responders to SSRI treatment did not choose to pursue subsequent cognitive behavior
therapy (Wisniewski et al., 2007). Perhaps parents who began with behavioral treatment
were more motivated to engage because they had not already dealt with several of weeks of
problem behavior at school without having received the parenting toolkit provided in BPT.
Alternatively, perhaps parents who began with medication, which requires minimal effort
and time, were reluctant to participate in more effortful and time-consuming parent training,
a major portion of which is learning to provide home backup for the school DRC. It is
possible that the teachers were less engaged in second-stage behavioral interventions
following initial medication for the same reasons as parents. Other researchers have reported
difficulties with engagement and attendance in behavioral treatments for ADHD (see for
example Barkley et al., 2000). Additional research is necessary to elucidate the mechanisms
of this problem with treatment engagement. Whatever the mechanism, the clinical
implications are quite clear: if providers intend for the parents of ADHD children to receive
parent training and for teachers to provide “extra” classroom management for the child (i.e.,
for the child to receive multimodal treatment) but start treatment with medication, they
reduce the likelihood of engagement in behavioral treatment and thus negatively impact
treatment outcome. Unfortunately, standard practice among physicians is to provide
medication immediately rather than delay it until the completion of parent training. The
results of the present study suggest this is a poor strategy.
With respect to our second question/Aim—which of the four embedded treatment protocols
produces the best outcomes?—it should be noted that each initial assignment is associated
with two embedded protocols, specifically those that began with that particular modality
(i.e., for BehFirst, the BB and BM protocols). With this in mind, the comparisons of
treatment protocols suggest that the primary driver of the first-stage treatment main effect
was the discrepancy between the two combined protocols, BM and MB. As Table 4 shows,
the two protocols involving increasing the initial intervention (BB and MM) produced
generally comparable results (though BB was superior to MM on classroom observations),
but the BM combined-treatment protocol was significantly more effective than the MB
protocol. The former produced the best outcomes on all but two variables, while the latter
produced the worst outcomes on all but two variables. Children following a combined
treatment protocol that began with behavioral treatment were superior on measures of
classroom observations, disciplinary actions, and teacher and parent ratings of ODD. Not
surprisingly, the normalization rates on teacher ratings in the school setting in the children
receiving multimodal treatment was double for the B-then-M (80%) versus M-then-B (38%)
groups, whereas normalization rates for the B-then-B and M-then-M protocols were nearly
identical (61% and 63%). This finding illustrating the superiority of BM over MB has
substantial clinical implications, shedding light on how sequencing of intervention can
enhance (or inhibit) engagement within an evidence-based intervention. For example, the
results of the present study suggest that the failure to begin behavioral treatment before
medication may have contributed to the relatively small advantage of combined treatment to
medication alone in the MTA study.
With respect to our third question/Aim, how to best augment treatment given insufficient
response to initial treatment, Table 5 shows that a low dose of medication is a useful
Pelham et al. Page 17
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
adjunctive intervention to add to initial behavioral treatment in the case of insufficient
response, as is increasing the intensity of behavioral intervention. Both additions were
helpful on a range of measures. In contrast, given insufficient response to medication,
increasing the dose of medication was superior across measures to adding behavioral
treatment (Table 6). This finding has important clinical implications. Once medication has
been employed, it appears that only a higher medication dose results in continued
improvement when more treatment is needed. As discussed above, the reasons for this may
be related to failure of parents (or teachers) to engage in behavioral treatments when they
follow medication. It should be noted that these comparisons involved small Ns, and power
to detect differences was limited. Further, a treatment regimen that includes only medication
is not a viable long-term treatment strategy for ADHD children, as it confers no long-term
benefit (Molina et al., 2009).
Furthermore, with regard to the secondary treatments involving adaptive medication,
children in the B-then-M group were taking significantly lower doses of medication at
school than children in the M-then-M condition. This finding indicates that beginning
treatment with behavior modification serves to decrease the necessary dose when medication
is used, which will result in lower levels of dose-related side effects (cf. Swanson et al,
2006).
Taken altogether, our results replicate and extend in the school-year environment what we
have reported in earlier studies conducted in analogue summer program settings (Fabiano et
al., 2007; Pelham et al., 2005; Pelham et al., 2014). Namely, a low dose of behavioral
treatment—in this study eight sessions of large group BPT and establishing a DRC at school
— is effective and sufficient for a substantial number of children with ADHD in school,
recreational, and home settings. Further, a low dose of medication (.15 mg/kg/dose b.i.d.)
was sufficient in the school setting for 53% of the children. Low doses of the two modalities
in combination were very effective for insufficient responders, but only when the behavioral
treatment came first. Neither our previous studies nor the MTA sequenced interventions, but
these results provide clear guidance about which sequence should be followed when
implementing combined treatment—BM rather than MB.
This is the first study to our knowledge that has addressed the effectiveness of such low-dose
interventions as a starting treatment for ADHD implemented in a community/school/clinic
setting. Low dose medication was sufficient in the school setting for a year for nearly half of
the children, but providing it first limited the effectiveness of additional behavioral treatment
when necessary. Further, the cost of the MedFirst condition and its protocols in the study
were far higher than BehFirst and its associated protocols (Page et al, this issue). Thus, the
study demonstrates that starting with a low dose of behavioral treatment and either
enhancing behavioral treatment or adding medication when necessary produces better
outcomes and is a far less costly approach to treatment for ADHD than starting with
medication (Page et al, this issue). Others have found increased side effects and reduced
tolerability as the dose and duration of medication increases (Barkley, McMurray,
Edelbrock, & Robbins., 1990; Stein et al., 2003; Pelham, et al, 1999; Swanson et al. 2006).
An adaptive approach in clinical practice that begins with low intensity behavior
modification and increases intensity or adds medication adaptively would appear to be the
Pelham et al. Page 18
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
treatment approach of choice for children with ADHD. The MTA had previously shown that
medication dose escalations over time are less necessary when multimodal treatment is
being implemented compared to medication alone (Vitiello et al., 2001), but the present
results extend that finding to considerably lower and less costly (Page et al., this issue) doses
of both medication and behavioral treatment than employed in the MTA and most other
studies in the ADHD treatment field.
Importantly, the adaptive nature of the approach to behavioral intervention was effective in
this study in producing very positive outcomes with relatively low intensity interventions for
most children and enhanced interventions for a small subset. For example, the adaptive
behavioral treatments employed in the school setting were carried out by the general
education teacher without additional intervention staff in two-thirds of the cases, and in only
six cases was an intensive paraprofessional-based program implemented. In contrast, in the
MTA a half-day paraprofessional—a very costly intervention—was provided for nearly a
full semester for all participants regardless of need. The present results suggest that that was
unnecessary for the vast majority of the children. These findings illustrate the utility of the
adaptive treatment approach, in which children only receive the types and levels of treatment
they require based on individual impairment. As discussed in the companion to this paper
(Page et al., this issue), the BB protocol was the least costly of the four protocols and the
BM protocol a close second. Thus, our effectiveness results and costs show a far different
picture than presented in the only other comparative study of cost-effectiveness in the
ADHD literature (cf. Jensen et al, 2005). These findings have important implications for the
public health system and insurance companies with regard to treatment costs for ADHD and
call for a reassessment of federal, insurer, and medical society recommendations on
treatments for ADHD, which currently prioritize medication and limit the extent to which
behavioral treatments can be utilized.
Limitations
It is important to note that this was an effectiveness study carried out in the natural
environment and therefore strict experimental control over the behavioral interventions
could not be exerted. Given the prevalence of classroom management training in schools,
teachers in the medication–only group were no doubt routinely implementing behavioral
strategies to manage their classrooms, and parents in the medication-only group may have
been implementing behavioral practices such as time out. Some of the lack of differences in
behavioral treatments may thus be due to the natural presence of behavioral treatments in
school and home settings. In addition, it was not possible to collect measures of parents’ in-
home implementation of procedures such as rewarding the Daily Report Card. Furthermore,
although observers completed checks of treatment integrity and fidelity during their
observations, they were often unable to observe teachers’ implementation of specific
procedures that were to be implemented (e.g., tracking DRC targets and giving feedback to
the child). We therefore were unable to calculate specific data for the fidelity of teacher-
delivered interventions. In contrast, medication was provided with greater experimental
control, with dosing practices varying some from what is done in routine clinical practice in
order to systematically assess sequencing effects across settings. For example, initial
Pelham et al. Page 19
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
medication treatments focused on school only, with evidence of objective impairment
required to be eligible for additional treatment at school or at home.
As noted above, many more children met criteria for additional treatment in the home setting
than in the school setting. In part this may be due to the initial treatment conditions. For
example, medication was initially provided only at school for MedFirst.. This approach
exposed those families to the impact of medication on their child at school and may have led
parents to rate their child as needing medication at home in order to obtain medication for
the home setting. The BPT program for BehFirst families was a brief, group-based
program–as opposed to the individually developed DRC for each child–and was sufficient
for only a subset of families. Others needed more individually-focused BPT, although the
amount required was relatively little except for a small subset of families. The lack of a
parallel, home-based DRC criterion (like the one used in the school setting) for additional
treatment also made it easier for children to meet criteria for additional treatment at home
relative to the criteria for allocating additional treatment at school. That is, parents simply
needed to indicate that their child was having problems and needed more treatment, whereas
teacher indication of need and ITBE target goal attainment rate below 75% were required at
school. This may also explain the somewhat contrary findings that BehFirst resulted in
superior outcomes relative to MedFirst, but the majority of children in the study met criteria
for rerandomization in the home setting. Future studies that utilize similar ITBE goal
attainment strategies in the home in addition to parent ratings may yield different outcomes
that are more similar to that obtained in the school setting in this study.
Another limitation relates to our study design, which included a maximum of two
randomizations per child. Ideally additional decision points might be included. For example,
in our protocol a child in need of adaptive second stage behavioral treatment could receive
ad lib treatment, as opposed to systematically limited, incrementally larger “doses” of
behavioral intervention, which might have been sufficient. Alternatively, for a child who is
still doing poorly after the first rerandomization, another opportunity to cross over to the
other treatment might be considered. For example, rather than a temporary classroom
paraprofessional, medication might have been considered at a third randomization for the
small number of children who require that level of assistance. This may be particularly true
in the case of nonadherence to the assigned treatments. The sample size required for
additional decision points precluded such considerations for the current study. Inclusion
criteria also required attendance within general education classrooms, so whether these
results generalize to self-contained special education classrooms is not known.
A final limitation was statistical power to address secondary aims. The study was fully
powered only for examination of the main effect of first-stage treatment (question/Aim 1).
We did not expect so many children to respond to first stage treatments and not need
additional treatment, so we did not plan for a larger N. Thus our power was reduced for
pairwise comparisons of the embedded treatment protocols (question/Aim 2), and then
further reduced for the comparison of treatments among insufficient responders
(question/Aim 3). Thus, numerous small to moderate effect sizes did not achieve statistical
significance but might have with a larger sample.
Pelham et al. Page 20
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Future Research
Finally, the adaptive methodology employed is promising for future studies of interventions
for ADHD in the pursuit of treatment tailoring for individual differences in functional
deficits. Replication of these results with a larger sample would afford better power and the
opportunity to investigate mediators and moderators for the treatment results that we
reported herein, including individual differences in comorbidity and impairment. For
example, why did parents whose children received medication first have such dramatically
reduced rates of uptake of parent training and associated poor outcomes relative to the other
protocols? Why did so many more children meet criteria for rerandomization at home
compared to school? Since combined treatment starting with behavior modification was so
effective, a natural follow-up question is how psychologists and other psychosocial mental
health and school-based providers can collaborate with M.D. prescribers in practice settings
to implement the conjoint strategies that were shown in this paper to be effective. Finally,
how might these interventions and approaches have to change to be effective with samples of
ADHD children both younger and older than our elementary-aged sample?
Clinical Implications
The results have direct relevance for clinical practice. The relatively low-dose-treatment
strategies that we employed are implementable in community mental health, primary care,
and school settings. The results suggest that practitioners should initiate treatment with low
doses of intervention, increasing intensity only when indicated. Many children will respond
sufficiently to low doses of initial treatments. Further, practitioners who initiate treatment
with behavioral intervention (group parent training and a school DRC) will produce better
outcomes for their patients than those starting with medication. In the face of inadequate
response to initial behavioral treatment, either more intensive behavioral intervention or the
addition of a low dose of medication for school hours will produce improved patient and
family outcomes. In contrast, if medication is the first stage treatment and is insufficient,
adding behavioral treatment is not an effective treatment option–outcomes are worse than
other strategies and parent engagement in subsequent parent training is very poor. Physicians
in particular need to be aware of these facts—simply advising their patients who have started
medication to go to a psychologist for parent training will not result in the desired outcome
—that is, a multimodal intervention. This paper and our companion paper on the costs of
these interventions strongly suggest a reconsideration of the current practice of relying on
medication as first line and typically sole treatment for many ADHD children. These results
suggest that a stepwise approach to treatment, starting with low doses of behavioral
treatment and increasing in intensity or adding medication only if necessary, would be a
cost-effective public health strategy for treatment of childhood ADHD in school and
community settings.
Acknowledgments
Funding
This research was funded by a grant from the Institute of Education Sciences (R324B060045). Dr. Pelham was also
supported in part by grants from the National Institute of Mental Health (MH069614, MH069434, MH092466,
MH53554, MH065899, MH62988), the Institute of Education Sciences (R324J060024, LO30000665A), the
Pelham et al. Page 21
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
National Institute of Alcohol Abuse and Alcoholism (AA11873), and the National Institute on Drug Abuse
(DA12414, DA12986).
References
AACAP Work Group on Quality Issues. Practice parameter for the assessment and treatment of
children and adolescents with attention deficit/hyperactivity disorder. Journal of the American
Academy of Child and Adolescent Psychiatry. 2007; 46(7):994–921. DOI: 10.1097/chi.
0b013e318054e724
APA Working Group on Psychoactive Medications for Children and Adolescents. Report of the
Working Group on Psychoactive Medications for Children and Adolescents.
Psychopharmacological, psychosocial, and combined interventions for childhood disorders:
Evidence base, contextual factors, and future directions. Washington, DC: American Psychological
Association; 2006. Available at: http://www.apa.org/pi/cyf/childmeds/pdf
Barkley RA, McMurray MB, Edelbrock CS, Robbins K. Side effects of methylphenidate in children
with attention deficit/hyperactivity disorder: A systematic, placebo-controlled evaluation. Pediatrics.
86:184–192. [PubMed: 2196520]
Barkley RA, Shelton TL, Crosswait C, Moorehouse M, Fletcher K, Barrett S, … Metevia L. Multi-
method psycho-educational intervention for preschool children with disruptive behavior:
Preliminary results at post-treatment. Journal of Child Psychology and Psychiatry and Allied
Disciplines. 2000; 41:319–332.
Barrish HH, Saunders M, Wolf MM. Good Behavior Game: Effects of individual contingencies for
group consequences on disruptive behavior in a classroom. Journal of Applied Behavior Analysis.
1969; 2:119–124. [PubMed: 16795208]
Briesch AM, Volpe RJ, Ferguson TD. The influence of student characteristics on the dependability of
behavioral observation data. School Psychology Quarterly. 2014; 29:171–181. DOI: 10.1037/
spq0000042 [PubMed: 24274156]
Cohen, J. Statistical Power Analysis for the Behavioral Sciences. 2. Hillsdale, NJ: Lawrence Erlbaum
Associates, Inc; 1988.
Collins LM, Murphy SA, Bierman KL. A conceptual framework for adaptive preventive interventions.
Prevention Science. 2004; 5:185–196. DOI: 10.1023/B:PREV.0000037641.26017.00 [PubMed:
15470938]
Collins LM, Schafer JL, Kam CM. A comparison of inclusive and restrictive strategies in modern
missing data procedures. Psychological Methods. 2001; 6(4):330–51. DOI: 10.1037/1082-989X.
6.4.330 [PubMed: 11778676]
Conduct Problems Prevention Research Group. Initial impact of the fast track prevention trial for
conduct problems: I. The high-risk sample. Journal of Consulting and Clinical Psychology. 1999a;
67:631–647. DOI: 10.1037/0022-006X.67.5.631 [PubMed: 10535230]
Conduct Problems Prevention Research Group. Initial impact of the fast track prevention trial for
conduct problems: II: Classroom effects. Journal of Consulting and Clinical Psychology. 1999b;
67:648–657. DOI: 10.1037/0022-006X.67.5.648 [PubMed: 10535231]
Conners CK. Forty years of methylphenidate treatment in Attention-Deficit/ Hyperactivity Disorder.
Journal of Attention Disorders. 2002; 6(Suppl 1):S17–S30. [PubMed: 12685516]
Coxe S, West SG, Aiken LS. The analysis of count data: A gentle introduction to Poisson regression
and its alternatives. Journal of Personality Assessment. 2009; 91(2):121–136. [PubMed:
19205933]
Cunningham, CE.; Bremner, R.; Secord-Gilbert, M. The community parent education (COPE)
program: A school-based family systems oriented course for parents of children with disruptive
behavior disorders. Chedoke-McMaster Hospitals and McMaster University; 1998.
Epstein JN, Kelleher KJ, Baum R, Brinkman WB, Peugh J, Gardner W, Langberg J. Variability in
ADHD care in community-based pediatrics. Pediatrics. 2014; 134:1136–1143. DOI: 10.1542/peds.
2014-1500 [PubMed: 25367532]
Evans SW, Owens JS, Bunford N. Evidence-based psychosocial treatments for children and
adolescents with attention-deficit/hyperactivity disorder. Journal of Clinical Child and Adolescent
Psychology. 2014; 43:527–551. DOI: 10.1080/15374416.2013.850700 [PubMed: 24245813]
Pelham et al. Page 22
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Fabiano GA, Pelham WE, Coles EK, Gnagy EM, Chronis AM, O’Connor B. A meta-analysis of
behavioral treatments for attention-deficit/hyperactivity disorder. Clinical Psychology Review.
2009; 29(2):129–140. DOI: 10.1016/j.cpr.2008.11.001 [PubMed: 19131150]
Fabiano GA, Pelham WE, Gnagy EM, Burrows-MacLean L, Coles EK, Chacko A, … Robb JA. The
single and combined effects of multiple intensities of behavior modification and methylphenidate
for children with Attention Deficit Hyperactivity Disorder in a classroom setting. School
Psychology Review. 2007; 36:195–216.
Fabiano GA, Pelham WE, Waschbusch DA, Gnagy EM, Lahey BB, Chronis AM, … Burrows-
MacLean L. A practical measure of impairment: psychometric properties of the impairment rating
scale in samples of children with attention deficit hyperactivity disorder and two school-based
samples. Journal of Clinical Child and Adolescent Psychology. 2006; 35:369–385. DOI: 10.1207/
s15374424jccp3503_3 [PubMed: 16836475]
Fabiano GA, Schatz NK, Aloe AM, Chacko A, Chronis-Tuscano AM. A review of meta-analyses of
psychosocial treatment for attention-deficit/hyperactivity disorder: systematic synthesis and
interpretation. Clinical Child and Family Psychology Review. 2015; 18:77–97. [PubMed:
25691358]
Fabiano GA, Vujnovic R, Naylor J, Pariseau M, Robins ML. An investigation of the technical
adequacy of a daily behavior report card (DBRC) for monitoring progress of students with
attention-deficit/hyperactivity disorder in special education placements. Assessment for Effective
Intervention. 2009; 34:231–241.
Fabiano GA, Vujnovic R, Pelham WE, Waschbusch DA, Massetti GM, Yu J, … Volker M. Enhancing
the effectiveness of special education programming for children with ADHD using a daily report
card. School Psychology Review. 2010; 39:219–239.
Greenhill LL, Pliszka S, Dulcan MK. the Work Group on Quality Issues. Practice parameter for the use
of stimulant medications in the treatment of children, adolescents, and adults. Journal of the
American Academy of Child & Adolescent Psychiatry. 2002; 41:26S–49S. DOI:
10.1097/00004583-200202001-00003 [PubMed: 11833633]
Greenhill LL, Abikoff HB, Arnold LE, Cantwell DP, Conners CK, Elliott G, … Wells K. Medication
treatment strategies in the MTA study: Relevance to clinicians and researchers. Journal of the
American Academy of Child & Adolescent Psychiatry. 1996; 35:1304–1313. DOI:
10.1097/00004583-199610000-00017 [PubMed: 8885584]
Gresham, FM.; Elliott, SN. Social Skills Rating System: Parent, teacher, and child forms. Circle Pines,
MN: American Guidance Systems; 1989.
Jacob, R.; Pelham, WE. Behavior therapy. In: Sadock, B.; Sadock, V., editors. Comprehensive
Textbook of Psychiatry. 7. New York: Williams & Wilkins; 1999. p. 2080-2127.
Jensen PS, Garcia JA, Glied S, Crowe M, Foster M, Schlander M, … Wells K. Cost-effectiveness of
ADHD treatments: findings from the multimodal treatment study of children with ADHD.
American Journal of Psychiatry. 2005; 162(9):1628–1636. [PubMed: 16135621]
Kleinke, K.; Reinecke, J. countimp 1.0 – A multiple imputation package for incomplete count data.
University of Bielefeld, Faculty of Sociology; 2013. [Technical Report]available from www.uni-
bielefeld.de/soz/kds/pdf/countimp
Lavori PW, Dawson R. A design for testing clinical strategies: biased individually tailored within-
subject randomization. Journal of the Royal Statistical Society: Series A (Statistics in Society).
2000; 163:29–38. DOI: 10.1111/1467-985X.00154
Molina BSG, Hinshaw SP, Swanson JM, Arnold LE, Vitiello B, Jensen PS. … the MTA Cooperative
Group. The MTA at 8 years: Prospective follow-up of children treated for combined-type ADHD
in a multisite study. Journal of the American Academy of Child and Adolescent Psychiatry. 2009;
48:484–500. [PubMed: 19318991]
MTA Cooperative Group. 14-month randomized clinical trial of treatment strategies for attention
deficit hyperactivity disorder. Archives of General Psychiatry. 1999a; 56:1073–1086. DOI:
10.1001/archpsyc.56.12.1073 [PubMed: 10591283]
MTA Cooperative Group. Moderators and mediators of treatment response for children with attention-
deficit/hyperactivity disorder. Archives of General Psychiatry. 1999b; 56:1088–1096. DOI:
10.1001/archpsyc.56.12.1088 [PubMed: 10591284]
Pelham et al. Page 23
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
MTA Cooperative Group. National Institute of Mental Health multimodal treatment study of ADHD
follow-up: 24-month outcomes of treatment strategies for attention-deficit/hyperactivity disorder
(ADHD). Pediatrics. 2004a; 113(4):754–761. DOI: 10.1542/peds.113.4.754 [PubMed: 15060224]
Murphy SA. An experimental design for the development of adaptive treatment strategies. Statistics in
Medicine. 2005; 24:1455–1481. DOI: 10.1002/sim.2022 [PubMed: 15586395]
Nahum-Shani I, Qian M, Almirall D, Pelham WE, Gnagy B, Fabiano GA, … Murphy S. Experimental
design and primary data analysis methods for comparing adaptive interventions. Psychological
Methods. 2012; 17:457–477. DOI: 10.1037/a0029372 [PubMed: 23025433]
Owens EB, Hinshaw SP, Kraemer HC, Arnold LE, Abikoff HB, Cantwell DP, … Wigal T. Which
treatment for whom for ADHD? Moderators of treatment response in the MTA. Journal of
Consulting and Clinical Psychology. 2003; 71(3):540–552. DOI: 10.1037/0022-006X.71.3.540
[PubMed: 12795577]
Page TF, Fabiano GA, Greiner AR, Gnagy EM, Pelham WE III, Hart K, Coxe S, Waxmonsky JG,
Foster EM, Pelham WE Jr. Comparative cost analysis of sequential, adaptive, behavioral,
pharmacological, and combined treatments for ADHD. Journal of Clinical Child & Adolescent
Psychology. in press. this issue.
Pelham WE. The NIMH multimodal treatment study for ADHD: Just say yes to drugs alone? Canadian
Journal of Psychiatry. 1999; 44:981–990. [PubMed: 10637677]
Pelham WE, Aronoff HA, Midlam J, Shapiro C, Gnagy E, Chronis A, … Waxmonsky J. A comparison
of ritalin and adderall: Efficacy and time-course in children with attention-deficit/hyperactivity
disorder. Pediatrics. 1999; 103(4):1–14. [PubMed: 9917431]
Pelham WE, Burrows-MacLean L, Gnagy EM, Fabiano GA, Coles EK, Tresco KE, … Hoffman MT.
Transdermal methylphenidate, behavioral, and combined treatment for children with ADHD.
Experimental and Clinical Psychopharmacology. 2005; 13(2):111–126. DOI:
10.1037/1064-1297.13.2.111 [PubMed: 15943544]
Pelham WE, Burrows-MacLean L, Gnagy EM, Fabiano GA, Coles EK, Wymbs BT, … Waschbusch
DA. A dose-ranging study of behavioral and pharmacological treatment for children with ADHD.
Journal of Abnormal Child Psychology. 2014; 42:1019–1031. DOI: 10.1007/s10802-013-9843-8
[PubMed: 24429997]
Pelham WE, Burrows-MacLean L, Gnagy EM, Fabiano GA, Coles EK, Wymbs BT, … Coxe S.
Behavioral, pharmacological, and combined treatment for ADHD: A dose-ranging, between
groups analysis. 2015 Manuscript under review.
Pelham WE, Fabiano GA. Evidence-based psychosocial treatment for attention deficit/hyperactivity
disorder: An update. Journal of Clinical Child and Adolescent Psychology. 2008; 37(1):185–214.
DOI: 10.1080/15374410701818681
Pelham WE, Fabiano GA, Massetti GM. Evidence-based assessment of attention-deficit/hyperactivity
disorder in children and adolescents. Journal of Clinical Child and Adolescent Psychology. 2005;
34:449–476. DOI: 10.1207/s15374424jccp3403_5 [PubMed: 16026214]
Pelham WE, Gnagy EM, Greiner AR, Hoza B, Hinshaw SP, Swanson JM, … Baron-Myak C.
Behavioral vs. behavioral and pharmacological treatment in ADHD children attending a summer
treatment program. Journal of Abnormal Child Psychology. 2000; 28:507–526. DOI: 10.1023/A:
1005127030251 [PubMed: 11104314]
Pelham, WE.; Gnagy, EM.; Greiner, AR.; Waschbusch, DA.; Fabiano, GA.; Burrows-MacLean, L.
Summer Treatment Programs for Attention Deficit/Hyperactivity Disorder. In: Kazdin, AE.;
Weisz, JR., editors. Evidence-Based Psychotherapies for Children and Adolescents. 2. New York:
The Guilford Press; 2010. p. 277-292.
Pelham WE, Gnagy EM, Greenslade KE, Milich R. Teacher ratings of DSM-III-R symptoms of the
disruptive behavior disorders. Journal of the American Academy of Child and Adolescent
Psychiatry. 1992; 31:210–218. DOI: 10.1097/00004583-199203000-00006 [PubMed: 1564021]
Power, TJ.; Habboushe, D.; Karustis, JL. Homework Success for Children with ADHD: A family-
school intervention program. New York: Guilford Press; 2001.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical
Computing; Vienna, Austria: 2015. URL http://www.R-project.org/
Pelham et al. Page 24
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Schultz, BK.; Evans, SW. A Practical Guide for Implementing School-Based Interventions for
Adolescents with ADHD. New York, NY: Springer; 2015.
Shortreed SM, Laber E, Scott Stroup T, Pineau J, Murphy SA. A multiple imputation strategy for
sequential multiple assignment randomized trials. Statistics in Medicine. 2014; 33(24):4202–4214.
[PubMed: 24919867]
Stein MA, Sarampote CS, Waldman ID, Robb AS, Conlon C, Pearl PL, … Newcorn JH. A dose-
response ostudy of OROS methylphenidate in children with attention deficit/hyperactivity disorder.
Pediatrics. 2003; 112:e404.doi: 10.1542/peds.112.5.e404 [PubMed: 14595084]
Subcommittee on Attention-Deficit/Hyperactivity Disorder, Steering Committee on Quality
Improvement and Management. ADHD: Clinical practice guideline for the diagnosis, evaluation,
and treatment of Attention-Deficit/Hyperactivity Disorder in children and adolescents. Pediatrics.
2011; 128:2011–2654. DOI: 10.1542/peds.2011-2654
Swanson J, Greenhill L, Wigal T, Kollins S, Stehli A, Davies M, … Wigal S. Stimulant-related
reductions of growth rates in the PATS. Journal of the American Academy of Child & Adolescent
Psychiatry. 2006; 45:1304–1313. DOI: 10.1097/01.chi.0000235075.25038.5a [PubMed:
17023868]
Swanson JM, Hinshaw SP, Arnold LE, Gibbons R, Marcus S, Hur K. … the MTA Cooperative Group.
Secondary evaluations of MTA 36-month outcomes: Propensity score and growth mixture model
analyses. Journal of the American Academy of Child and Adolescent Psychiatry. 2007; 46:1003–
1014. DOI: 10.1097/CHI.0b013e3180686d63 [PubMed: 17667479]
Swanson JM, Kraemer H, Hinshaw SP, Arnold LE, Conners CK, Abikoff H, … Wu M. Clinical
relevance of the primary findings of the MTA: Success rates based on severity of ADHD and ODD
symptoms at the end of Treatment. Journal of the American Academy of Child and Adolescent
Psychiatry. 2001; 40:168–179. [PubMed: 11211365]
Van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate imputation by chained equations in R.
Journal of Statistical Software. 2011; 45(3)
Visser SN, Danielson ML, Bitsko BH, Holbrook JR, Kogan MD, Ghandour RM, … Blumberg SJ.
Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/
hyperactivity disorder: United States, 2003-2011. Journal of the American Academy of Child &
Adolescent Psychiatry. 2014; 53:34–46. DOI: 10.1016/j.jaac.2013.09.001 [PubMed: 24342384]
Vitiello B, Severe JB, Greenhill LL, Arnold LE, Abikoff HB, Bukstein OG, … Cantwell DP.
Methylphenidate dosage for children with ADHD over time under controlled conditions: Lessons
from the MTA. Journal of the American Academy of Child & Adolescent Psychiatry. 2001;
40:188–196. DOI: 10.1097/00004583-200102000-00013 [PubMed: 11211367]
Volpe, R.; Fabiano, GA. Daily Behavior Report Cards: An Evidence-Based System of Assessment and
Intervention. New York: The Guilford Press; 2013.
Vujnovic RK, Fabiano GA, Waschbusch DA, Pelham WE, Greiner A, Gera S, … Buck M. Preliminary
psychometric properties of an observation system to assess teachers/ use of effective behavior
support strategies in preschool classrooms. Education and Treatment of Children. 2014; 37:323–
346. DOI: 10.1353/etc.2014.0020
White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for
practice. Statistics in Medicine. 2011; 30(4):377–399. [PubMed: 21225900]
Wisniewski SR, Fava M, Trivedi MH, Thase ME, Warden D, Niederehe G, … John Rush A MD.
Acceptability of second-step treatments to depressed outpatients: a STAR* D report. The
American Journal of Psychiatry. 2007; 164(5):753–760. [PubMed: 17475734]
Pelham et al. Page 25
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Figure 1.
Participant Flow
Pelham et al. Page 26
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Figure 2.
Study Design
Pelham et al. Page 27
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Figure 3.
Means on Observed Classroom Rules Violations and Out-of-Class Disciplinary Events as a
Function of Treatment Decisions
Pelham et al. Page 28
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Figure 4.
Parent Training Attendance by Initial Treatment Assignment
Note. Figures for the Medication First families consider only those that were rerandomized
to behavioral treatment (M-then-B, N=35).
Pelham et al. Page 29
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Pelham et al. Page 30
Table 1
Sample Characteristics
Variable Medication First Behavioral First
Number of participants 74 72
Child Age in Years 8.3 (2.0) 8.5 (1.8)
Child Gender (% Male) 77% 75%
Child Race
White 76% 84%
Black/African American 17% 7%
Other 7% 8%
Child IQ 99.9 (16.2) 99.2 (12.5)
Other Diagnoses
Oppositional/Defiant Disorder 60% 54%
Conduct Disorder 17% 14%
ADHD Symptoms Endorsed
Inattention 7.6 (1.9) 8.1 (1.5)
Hyperactivity/Impulsivity 7.1 (2.2) 6.8 (2.1)
Parent Disruptive Behavior Disorders Rating
ADHD 1.89 (0.61) 1.99 (0.50)
ODD 1.32 (0.67) 1.29 (0.57)
CD 0.26 (0.28) 0.21 (0.20)
Teacher Disruptive Behavior Disorder Rating
ADHD 1.84 (0.62) 1.78 (0.60)
ODD 1.17 (0.84) 0.95 (0.73)
CD 0.45 (0.59) 0.31 (0.44)
Parental Marital Status (% Single Parent) 11% 7%
Highest Parental Education Level
High School Diploma or Less 10% 10%
Partial college or technical training 17% 14%
2-year degree 25% 19%
4-year degree 24% 31%
Graduate training 25% 26%
Previous Medication Treatment 27% 31%
Note. Groups did not differ significantly on any demographic measure.
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Pelham et al. Page 31
Table 2
Intervention Components
Modality Initial Treatment Secondary/Adaptive Treatment
Medication • 8-hour stimulant equivalent to 0.15
mg/kg methylphenidate b.i.d.
• Increased school dose
• Added evening/weekend doses
Behavioral Treatment
• 8 weekly sessions of group behavioral
parent training (concurrent group
social skills training for children)
• Monthly booster parent training
sessions
• 3 consultation meetings with primary
teacher to establish a school-home
daily report card
• One individual parent training session
to establish home rewards for daily
report card
• Group or individual classroom
contingency management systems
(Barrish, Sauders, & Wolf, 1969)
• Time-out in school
• Tutoring
• Organizational skills training (Schultz &
Evans, 2015)
• School-based rewards
• Weekly Saturday social skills sessions
(Pelham et al., 2008)
• Homework skills training (Power et al.,
2001)
• Paraprofessional-implemented school
rewards programs
• Home-based daily report card
Note. The adaptive components listed represent those offered or recommended as-needed based on individual areas of impairment. Not every child
received every component of the adaptive treatment.
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Pelham et al. Page 32
Table 3
Outcomes at Endpoint by Initial Treatment Assignment
Outcome Medication First Behavioral First Effect Size
Classroom rules violations per hour** 12.6 [10.5, 15.3] 8.4 [6.8, 10.3] IRR = 0.66
Out-of-class disciplinary events per school year† 3.1 [1.8, 5.2] 1.6 [0.9, 2.7] IRR = 0.52
Teacher DBD—ADHD 0.98 (.67) 1.00 (.64) d = −0.02
Teacher DBD—ODD 0.59 (.66) 0.45 (.51) d = 0.24
Teacher SSRS Social Skills Total Score 33.9 (9.5) 36.0 (10.5) d = 0.21
Parent DBD—ADHD 1.44 (.64) 1.45 (.62) d = −0.01
Parent DBD—ODD 1.09 (.71) 0.98 (.65) d = 0.16
Parent SSRS Social Skills Total Score 45.2 (10.8) 45.3 (10.7) d = 0.01
Note. IRR=incidence rate ratio, DBD=Disruptive Behavior Disorders Rating Scale (scores are average scale scores, range 0–3), ADHD=attention
deficit hyperactivity disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale. Values are means with standard deviations in
parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about the mean (for count outcomes). The IRR is the ratio of the
event (e.g., rule violation) incidence rate in one group (here, Behavioral First) to the incidence rate in another group (here, Medication First). The
other effect sizes are Cohen’s D with pooled standard deviation (equations 2.5.1 and 2.5.2, pp. 66–67, Cohen, 1988), and are listed such that a
positive d reflects an advantage of Behavioral First.
†
p<0.10,
**
p<0.01.
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Pelham et al. Page 33
Table 4
Outcomes at Endpoint by Treatment Protocol Followed
Outcome BB protocol BM protocol MB protocol MM protocol
Classroom rules violations per hour 7.2† [5.8, 9.0] 9.3a† [7.6, 11.4] 14.3b [11.1, 18.5] 12.7ab [9.0, 18.0]
Out-of-class disciplinary events per school year 2.6ab [1.1, 6.1] 0.9c [0.5, 1.7] 5.5a† [2.4, 12.9] 1.9bc† [0.9, 4.2]
Teacher DBD— ADHD 1.09 (.65)a 0.91 (.61)a 1.02 (.71)a 0.94 (.63)a
Teacher DBD— ODD 0.48 (.55)ab 0.42 (.46)a† 0.69 (.79)b† 0.50 (.50)ab
Teacher SSRS Social Skills Total Score 35.0 (10.8)ab 36.8 (10.0)a† 33.2 (10.7)b† 34.5 (8.2)ab
Parent DBD— ADHD 1.52 (.63)a 1.37 (.59)a 1.54 (.65)a 1.34 (.60)a
Parent DBD—ODD 1.10 (.69)ab† 0.86 (.58)c† 1.23 (.74)a‡ 0.95 (.64)bc‡
Parent SSRS Social Skills Total Score 44.2 (10.0)a 46.4 (11.2)a 45.0 (10.1)a 45.4 (11.3)a
Note. DBD=Disruptive Behavior Disorders Rating Scale (scores are average scale scores, range 0–3), ADHD=attention deficit hyperactivity
disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale. The first letter of each protocol indicates its first-stage treatment
and the second letter indicates its second-stage treatment, to be implemented in the event of insufficient response (‘B’ for behavioral, ‘M’ for
medication). Values are means with standard deviations in parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about
the mean (for count outcomes). They were calculated using the weighting method as described in Nahum-Shani et al. (2012). Within each row,
means that have no superscript in common are significantly different from each other, p<.05. Cross or doublecross next to a pair of means indicates
difference was only marginal, p<.10.
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Pelham et al. Page 34
Table 5
Outcomes at Endpoint by Secondary/adaptive Treatment Given Insufficient Response to Initial Behavioral
Treatment
Outcome B-then-B B-then-M Effect Size
Classroom rule violations per hour* 6.6 [5.1, 8.6] 9.4 [7.5, 11.7] IRR = 1.41
Out-of-class disciplinary events per school year† 3.2 [1.2, 8.3] 1.0 [0.4, 2.7] IRR = 0.30
Teacher DBD—ADHD 1.28 (.65) 1.00 (.65) d = 0.44
Teacher DBD—ODD 0.63 (.60) 0.52 (.49) d = 0.19
Teacher SSRS Social Skills Total Score 32.0 (9.6) 35.0 (9.1) d = 0.31
Parent DBD—ADHD 1.60 (.66) 1.43 (.63) d = 0.26
Parent DBD—ODD† 1.20 (.69) 0.90 (.59) d = 0.45
Parent SSRS Social Skills Total Score 41.8 (9.1) 44.4 (11.2) d = 0.26
Note. B-then-B=began with behavioral treatment and then received higher dose behavioral treatment, B-then-M=began with behavioral treatment
then added medication treatment, IRR=incidence rate ratio, DBD=Disruptive Behavior Disorders Rating Scale (scores are average scale scores,
range 0–3), ADHD=attention deficit hyperactivity disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale. Values are
means with standard deviations in parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about the mean (for count
outcomes). The IRR is the ratio of the event (e.g., rule violation) incidence rate in one group (here, B-then-M) to the incidence rate in another group
(here, B-then-B). The other effect sizes are Cohen’s D with pooled standard deviation (equations 2.5.1 and 2.5.2, pp. 66–67, Cohen, 1988), and are
listed such that a positive d reflects an advantage of B-then-M.
†
p<0.10,
*
p<0.05.
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
A
utho
r M
anuscrip
t
A
utho
r M
anuscript
Pelham et al. Page 35
Table 6
Outcomes at Endpoint by Secondary/adaptive Treatment Given Insufficient Response to Initial Medication
Treatment
Outcome M-then-M M-then-B Effect Size
Classroom rule violations per hour 14.5 [9.5, 22.1] 17.1 [10.9, 26.9] IRR = 1.18
Out-of-class disciplinary events per school year† 2.2 [0.8, 6.6] 8.2 [3.5, 19.6] IRR = 3.66
Teacher DBD—ADHD 1.21 (.63) 1.43 (.71) d = −0.34
Teacher DBD—ODD† 0.70 (.52) 1.15 (.91) d = −0.61
Teacher SSRS Social Skills Total Score 32.2 (6.2) 28.8 (11.0) d = −0.39
Parent DBD—ADHD 1.38 (.60) 1.62 (.63) d = −0.38
Parent DBD—ODD† 1.02 (.65) 1.33 (.73) d = −0.46
Parent SSRS Social Skill Total Score 44.5 (11.2) 44.0 (9.6) d = −0.05
Note. M-then-M=began with medication treatment and then received higher dose medication treatment, M-then-B=began with medication
treatment and then added behavioral treatment, IRR=incidence rate ratio, DBD=Disruptive Behavior Disorders Rating Scale (scores are average
scale scores, range 0–3), ADHD=attention deficit hyperactivity disorder, ODD=Oppositional defiant disorder, SSRS=Social Skills Rating Scale.
Values are means with standard deviations in parentheses (for continuous outcomes) or asymmetric 95% confidence intervals about the mean (for
count outcomes). The IRR is the ratio of the event (e.g., rule violation) incidence rate in one group (here, M-then-B) to the incidence rate in another
group (here, M-then-M). The other effect sizes are Cohen’s D with pooled standard deviation (equations 2.5.1 and 2.5.2, pp. 66–67, Cohen, 1988),
and are listed such that a positive d reflects an advantage of M-then-B.
†
p<0.10.
J Clin Child Adolesc Psychol. Author manuscript; available in PMC 2017 July 01.
Essay Writing Service Features
Our Experience
No matter how complex your assignment is, we can find the right professional for your specific task. Achiever Papers is an essay writing company that hires only the smartest minds to help you with your projects. Our expertise allows us to provide students with high-quality academic writing, editing & proofreading services.
Free Features
Free revision policy
$10Free bibliography & reference
$8Free title page
$8Free formatting
$8How Our Dissertation Writing Service Works
First, you will need to complete an order form. It's not difficult but, if anything is unclear, you may always chat with us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download