In a short essay format, 1-2 single-spaced pages, write one side of a debate about the safety of oral contraceptives based on this study. Choose either to support the author’s conclusion, or to argue that it is misguided. You should not base your debate on conjecture, pre-formed personal opinion, or even more recent findings from the Nurses’ Health Study, if you are aware of them – instead focus on the study itself and the decisions made in its design and conduction. As you formulate your argument, you may also include any possible impacts on public health or medical practice.
Debate about the safety of oral contraceptives
Instructions
By the end of this activity, you will be able to identify and discuss advantages and disadvantages of a cohort study design surrounding a controversial public health topic. Additionally, you will be able to present persuasive conclusions in an objective manner by utilizing knowledge of study design to support and defend your position of choice, and synthesize the findings of a landmark cohort study from multiple perspectives.
Before beginning this activity, review the faculty notes and readings in the text.
As you read the article, “
Oral Contraceptive Use and Mortality during 12 Years of Follow-Up: The Nurses’ Health Study.
” I would like you to relate the contents of the article to the faculty notes and readings, paying particular attention to the components of the cohort study design as well as the practical implications of the study. The safety of oral contraceptives has been hotly debated with regards to many health outcomes, including mortality and cancer. This paper was one of many produced from the findings of the Nurses’ Health Study, a large-scale prospective cohort study of female registered nurses which first began follow-up in 1976.
It is understood that you may have minimal background in some of the medical terminology and statistical methods described in the paper. However, comprehending these components fully is not critical to evaluating the study design.
As you read, think about the reasons an epidemiologist would choose to use a cohort design. What are the advantages/disadvantages or a cohort study specific to the health outcome (mortality) and exposure (oral contraceptives)? Think about whether they are common or rare, easy or difficult to measure, and if the findings might apply to the population in general.
As you read, think about the potential public health implications of a study like this. Consider the author’s conclusion (in the very last paragraph), and whether you think the study design was strong enough (and free of bias) to justify their statement.
Studies such as this often make headlines and have the potential to impact treatment of millions of individuals. How might the study’s cohort design (compared to other options you have learned about) been an advantage in testing the study’s hypothesis? How might the findings from this study have affected standards of care (the normal diagnostic or treatment process for a specific condition) for contraception needs?
In a short essay format, 1-2 single-spaced pages, write one side of a debate about the safety of oral contraceptives based on this study. Choose either to support the author’s conclusion, or to argue that it is misguided. You should not base your debate on conjecture, pre-formed personal opinion, or even more recent findings from the Nurses’ Health Study, if you are aware of them – instead focus on the study itself and the decisions made in its design and conduction. As you formulate your argument, you may also include any possible impacts on public health or medical practice. The faculty notes and readings present the basic issues for the cohort design that can help you to guide your essay. In addition, use the points below to help you to write an organized, concise argument.
· The basic design and structure of the cohort, including inclusion/exclusion criteria, how it was identified and followed. Think about possible sources of bias, or information that may have been useful but was not collected, or why you think bias was unlikely to have been operative.
· Think about how the choice of a cohort design compares to other design options in testing the study’s hypothesis.
· Consider that the findings from this study may affect standards of care (the normal diagnostic or treatment process for a specific condition) for contraception needs, and what impacts that may have (how many people affected, costs, etc.).
· Based on what you have learned about the strengths of a cohort design and the findings of the study, determine if you think the author’s conclusion is justified. Decide if there be anything you might do differently or any additional study you might prefer to conduct to further support these findings.
· Based on what you have learned about the strengths of a cohort design and the findings of the study, determine if you think the author’s conclusion is justified, or needs more support.
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ECOLOGICAL ST UDIES AND CROSS-SECTION AL STUDIE S
Apart from experimental studies, all other study designs in epidemiology are observational designs. We now
begin a summary of two such designs.
Ecologic Studies
Ecologic studies examine disease rates in relation to a factor (exposure) described on the population level.
Ecologic studies often identify groups by place (e.g., county, state, and country) or time
period
, and often
both. This means the units of analysis are groups of people rather than individuals, and comparisons are
usually of place or time. The population-level factor summarizes information for all individuals within a specific
population. For example, an ecologic study might examine the relation between county-level air pollution and
mortality rates. We may also compare disease rates for a geographically defined population over time, such as
HIV seroprevalence over time and needle exchange programs (yes/no). Exposure and outcome are measured
for the same time period. For example, we can compare mortality between 2 counties in New York State and
simultaneously examine the average level of an air pollutant for those two counties for the same time period.
An important source of bias in ecologic design is something called the ecologic fallacy. This is when an
association observed between variables on a group level does not necessarily represent the association that
exists on the individual level. For example, if we take an average air pollution measure for an entire county,
this may not actually be the level of pollution that every individual is exposed to. Some individuals may be
exposed to much less, others to much more.
The advantages of conducting ecologic studies are:
• The types of data commonly used are routinely collected for purposes other than conducting research,
thus reducing the cost of the study since the epidemiologist does not have to collect the data.
• Some exposures are inherently ecologic. For example in a community with a lot of air pollution sources
(factories, traffic, etc.), it may be easier to assign exposure on the group-level than for an individual.
• You can study many different types of exposures on one type of health outcome.
• You can study what we refer to as “contextual effects” on health, such as looking at the impact of
community-level poverty on health.
The major disadvantages of the ecologic design include:
• The quality of the data may vary. Since the data is typically not collected for conducting an
epidemiologic study but instead for other purposes, information may be missing or not as complete as
we would like.
• There is difficulty in identifying complex relationships. Since we are averaging information for a group,
we may “wash out” some relationships and not observe them.
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• Temporality is difficult to establish. When we measure both exposure and outcome at the same time,
we cannot be sure that exposure preceded the outcome. (Logically, if exposure does not precede
disease, it cannot be a causal factor for that disease.)
• The ecologic fallacy.
Cross-sectional Studies
Cross-sectional studies also examine a health outcome in relation to exposure in a defined population at a
specific period of time. These studies can be envisioned as a “snapshot” of exposure and disease – where
current disease status at a particular time is examined in relation to current exposure level during the same
time period. In other words, there is no follow-up over time. A common method of conducting a cross-
sectional study is via surveys, where questions regarding exposure and disease are asked of an identified
population. Cross-sectional designs are useful for public health planning purposes, such as identifying the
number of beds needed in a hospital or determining a community’s need for family planning services. They are
often useful for hypothesis generation, since they may provide clues as to possible risk factors for a condition
that can be better examined with a more rigorous study design.
The advantages of conducting cross-sectional studies are:
• Can typically be conducted over a short time period, reducing study cost.
• Good for hypothesis generation.
• If conducted among a sample of the population, we may say that study findings are generalizable.
The major disadvantages of the cross-sectional design include:
• The quality of the data may vary. Since the data is often not collected for conducting an epidemiologic
study but instead for other purposes, information may be missing or not detailed adequately.
• Cross-sectional studies usually are generally limited to identifying prevalent disease, because they
measure disease status at only one time point.
Just as in the ecologic design, temporality between exposure and disease is difficult to establish.
E
O
Measured during
the same time
period
E
O
Measured during
the same time
period
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EXPERI MENTAL E PIDE MIOLOGICAL STUDIE S
Epidemiologic studies are either observational or experimental. Observational studies, including ecologic,
cross-sectional, cohort, and case-control designs, are considered “natural” experiments, but experimental
studies are considered true experiments. We will spend the next 2 modules discussing these designs.
Before we begin to discuss study designs, we need a brief introduction to a concept that we will spend more
time discussing in later modules — bias. The definition of bias is:
“Deviation of results or inferences from the truth, or processes leading to such deviation. Any trend in the
collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are
systematically different from the truth.” (Last, J.M., A Dictionary of Epidemiology, 4th ed.)
Epidemiologists are naturally concerned whether the results of an epidemiologic study are biased, since many
important public health decisions are often drawn from epidemiologic research. The severity of the bias, that
is – how much it influences or distorts the results, is related to the study design as well as how information is
analyzed.
Experimental Studies
The defining feature of experimental studies is that the investigator assigns exposure to the study subjects.
Experimental studies most closely resemble controlled laboratory experiments and serve as models for the
conduct of observational studies, thus they are the “gold standard” of epidemiologic research. Experimental
studies have high validity (i.e., less bias), and can identify even very small effects. The most well known type of
experimental study is a randomized trial (sometimes referred to as a randomized controlled trial), where the
investigator randomly assigns exposure to the study subjects. In this type of study, the only expected
difference between the experimental and control groups is the outcome variable being studied.
Experimental designs like the randomized trial can assess both preventive interventions, where a prophylactic
agent is given to healthy or high-risk individual to prevent disease, or can assess effects of therapeutic
treatment, such as those given to diseased individuals to reduce their risk of disease recurrence, or to improve
their survival or quality of life.
Preventive intervention: Does tamoxifen lower the incidence of breast cancer in women with high risk profile
compared to high risk women not given tamoxifen?
Therapeutic intervention: Do combinations of two or three antiretroviral drugs prolong survival of AIDS
patients as well as regimens of single drugs?
The investigator can assign exposures (or allocate interventions) to either individuals or to an entire
community.
Individual-level assignment: Do women with stage I breast cancer given a lumpectomy alone survive as long
without recurrence of disease as women given a lumpectomy plus radiation?
Community-level assignment: Does fluoride in the water supply decrease the frequency of dental caries in a
community compared to a similar community without such water treatment?
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An experimental study is conducted in a few basic steps:
• First, a hypothesis is formed
• Study subjects are then recruited based on specific eligibility criteria and their informed consent to
participate is sought
• Eligible and willing subjects are randomly allocated to receive one of the two or more interventions
being compared
• Study groups are then monitored for the outcome under study (e.g., recurrence of disease, first
occurrence of disease, getting better, side effects)
• Rates of the outcome in the various groups are compared
The defining feature of an experimental study such as a randomized trial is that participants are randomly
assigned to their exposure status by the investigator. They do not have the health outcome of interest at the
start of the study. For example, in a drug trial, many participants have disease, but the outcome of interest is a
reduction in symptoms or prevention of disease recurrence. Participants are assigned exposure (exposed/ not
exposed) and followed to see if they get the outcome. The investigator is interested in the moment a person
goes from outcome-free to having the outcome.
Important Concepts in Experimental Study Design
Eligibility criteria include restrictions on who is eligible to participate. If examining a preventive therapy, we
may insist that participants be outcome-free at the study start. However, if the goal is to evaluate a treatment
aimed to reduce symptoms or even cure disease, the investigator may require that participants have the
disease of interest to be eligible. With regard to eligibility, the investigator must think about: 1) what group of
individuals is of interest, 2) logistically, what group is accessible to the researcher, will comply with the study
protocol, and can be most completely followed, and 3) how the study population differs from the reference
population to which study findings will be generalized. This reference population is the general group to whom
results of a trial should be applicable (such as all humans, or only HIV-positive individuals, or only menopausal
women).
E
O
O
O
O
O
O
O
O
E
At the beginning,
all participants are
outcome-free
and at risk for the outcome
Investigator
assigns
exposure
randomly
Time
(ex: Drug)
(ex: Placebo)
Who among the unexposed
group gets the outcome?
Who among the exposed
group gets the outcome?
E = exposed, E = unexposed (not exposed)
O = has outcome, O = does not have outcome
EE
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Treatment allocation should be randomly assigned. Random assignment means that each individual has the
same chance of receiving each possible treatment (the intervention or the placebo). Random allocation can be
assigned in several ways, such as via random number table, or even a coin toss. A non-random treatment
assignment, such as alternately assigning treatment or placebo as participants enroll, can lead to bias and
potential imbalances between the study groups with respect to important risk factors.
Randomization. The goal of randomization is to achieve baseline comparability between compared groups on
factors potentially related to outcome other than treatment. This is because the essence of good comparison
between treatments is that the compared groups are the same except for the treatment. Any group of
individuals will vary in response to a treatment based upon their sex, age, overall health, severity of illness,
and other factors. The investigator may already know about some of these, but there are many unknown
factors that are also relevant. The compared groups should have the same distribution of all of these
characteristics. That is what randomization can accomplish: the equal distribution of known and unknown
factors that are relevant to response to the treatment (these are called confounders, which will be discussed
in more detail in future modules).
Randomization example: Distribution of age and race in a randomized trial of maternal-infant HIV transmission
Zidovudine Group
N = 239
Placebo Group
N = 238
Median maternal age at entry 25 yrs 25 yrs
Percent White 42 38
Gestational age at entry
Percent 14-26 weeks 52 50
Percent > 26 weeks 48 50
Mean CD4 count at entry 560 538
Sample size refers to both the number of people enrolled in the trial, as well as how many endpoints
(outcomes under study) are expected. The larger the groups being compared, the better randomization works.
Ethical considerations
In order to conduct an experimental study in humans, there must be genuine doubt about efficacy of
treatment yet sufficient belief that it may work, a concept called equipoise. Having predetermined stopping
rules in place prior to the study’s conduction is also critical for ethical research. For example, what will the
investigators do if it becomes apparent, before the trial is over, that the new treatment is beneficial (and
should not be withheld from the placebo group)? Or alternatively, that the treatment is toxic and should be
withdrawn?
Placebos and blinding are used to make the study groups as comparable as possible. The placebo is sham
treatment designed to seem just like the real treatment (for example, a sugar pill or a saline injection instead
of the actual therapy). Blinding is when subjects do not know if subjects are receiving treatment or placebo
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(single blind). When neither subjects nor investigators know who is receiving treatment or placebo, the study
is called a double blind trial. The purpose of blinding is to avoid bias in ascertainment of outcome – if subjects
know they are receiving placebo, for example, they may choose not to comply with their treatment.
Maintenance and Assessment of Compliance
A randomized trial requires active participation and cooperation of participants, but deviations from the
protocol will occur related to side effects, illness, level of interest, and length of follow-up. Noncompliance
makes the compared groups more alike, which reduces the ability of the investigator to detect a difference
between the groups. Frequent contact with subjects and incentives to continue with the study (such as free
health check-ups) may improve compliance, as will blinding treatment status.
Ascertaining the Outcome of a Randomized Trial
A primary goal of the investigator for an experimental study is to have high follow-up rates. This means to
reduce the number of participants who are lost to follow up – such as when individuals drop out of the study
for various reasons, or cannot be reached due to invalid contact information. Another goal is to have uniform
follow-up for compared groups, so the investigator should strive to follow-up both groups with equal intensity.
This is because non-uniform ascertainment of outcome may lead to bias in the results.
Analysis of randomized trial data
Data set up: the familiar 2 x 2 table
Yes No Total
Treatment a b a+b
Placebo c d c+d
Total a+c b+d a+b+c+d
Measure of treatment effect: Relative Risk: (a/a+b) / (c/c+d) or Risk Difference: [a /(a+b)] – [c /(c+d)]
Planning for an informative result is important when analyzing experimental data. If the study finds no
difference between compared treatments, does the investigator believe it – or was there a difference but the
study was not large enough to detect it? Epidemiologists analyze by intent-to-treat: even though the study
may not have 100% treatment compliance by participants, data are typically analyzed by the treatment
assignment, regardless of whether it is known if the individual stops or continues treatment. The intent-to-
treat principle is rationalized because less than 100% compliance may break the randomization that is
designed to create comparable study groups.
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