Module 4 - Epidemiologic Study Designs 1:
Cohort Studies & Clinical Trials
Introduction
Video transcript in a Word file
We previously discussed descriptive epidemiology studies, noting that they are important for alerting us to emerging health problems, keeping track of trends in the population, and generating hypotheses about the causes of disease. Analytic studies provide a basic methodology for testing specific hypotheses. The essence of an analytic study is that groups of subjects are compared in order to estimate the magnitude of association between exposures and outcomes. This module will build on descriptive epidemiology and on measuring disease frequency and association by discussing cohort studies and intervention studies [clinical trials]. Our discussion of analytic study designs will continue in module 5 which addresses case-control studies. Pay particular attention to the strengths and weaknesses of each design. This is important for being able to select the most appropriate design to answer a given research question. In addition, a firm understanding of the strengths and weaknesses of each design will facilitate building your skills in critical reading of studies by alerting you to possible pitfalls and weaknesses that can undermine the validity of a study.
Essential Questions
- What are the different strategies for investigating the causes or sources of health outcomes?
- How do we choose the best approach to study a particular health problem?
- What are the strengths and limitations of different study designs?
Learning Objectives
After completing this section, you will be able to:
- Explain the role of descriptive epidemiology for defining health problems and establishing hypotheses about the determinants of health and disease.
- Explain the utility and the limitations of case reports and case series.
- Describe the design features and the advantages and weaknesses of each of the following study designs: Cross-sectional studies, ecological studies, retrospective and prospective cohort studies, case control studies, and intervention studies
- Identify the study design when reading an article or abstract.
- Explain how different study designs can be applied to the same hypothesis to provide different and complementary information.
Overview of Epidemiologic studies
The figure below provides a brief overview of epidemiologic studies. The descriptive studies that have already been discussed are listed in the top part: case reports and case series, cross-sectional studies, and ecologic studies. In addition to identifying new problems and keeping track of trends in a population, they also generate hypotheses that can be tested using one of the analytic studies shown at the bottom.
Note that cohort studies and case-control studies are observational studies, because investigators do not allocate exposure status. Some exposures are constituent [e.g., one's genome], some are behaviors and life style choices, and others are circumstantial, such as social, political, and economic determinants that affect health. None of these exposures are controlled by the investigators in observational studies; the investigators literally observe, collecting data on these exposures and on a variety of health outcomes. In contrast, intervention studies [also called clinical trials or experimental studies] are more like a true experiment in that the investigators assign subjects to a specific exposure [e.g., one or more treatment groups], and they are followed forward in time to record health outcomes of interest. Each of these analytic studies is useful in particular circumstances. Let's begin by discussing cohort studies.
Cohort Studies
Key Features of Cohort Studies
In cohort studies investigators enroll individuals who do not yet have the health outcomes of interest at the beginning of the observation period, and they assess exposure status for a variety of potentially relevant exposures. The enrollees are then followed forward in time [i.e., these are longitudinal studies rather than cross-sectional] and health outcomes are recorded. With this data investigators can sort the subjects according to their exposure status for one of the exposures of interest and compare the incidence of disease among the exposure categories.
For example, in 1948 the Framingham Heart Study enrolled a cohort of 5,209 residents of Framingham, MA who were between the ages of 30-62 and who did not have cardiovascular disease when they were enrolled. These subjects differed from one another in many ways: whether they smoked, how much they smoked, body mass index, eating habits, exercise habits, sex, family history of heart disease, etc. The researchers assessed these and many other characteristics or "exposures" soon after the subjects had been enrolled and before any of them had developed cardiovascular disease. The many "baseline characteristics" were assessed in a number of ways including questionnaires, physical exams, laboratory tests, and imaging studies [e.g., x-rays]. They then began "following" the cohort, meaning that they kept in contact with the subjects by phone, mail, or clinic visits in order to determine if and when any of the subjects developed any of the "outcomes of interest," such as myocardial infarction [heart attack], angina, congestive heart failure, stroke, diabetes and many other cardiovascular outcomes. They also kept track of whether their risk factors changed.
Over time some subjects eventually began to develop some of the outcomes of interest. Having followed the cohort in this fashion, it was eventually possible to use the information collected to evaluate many hypotheses about what characteristics were associated with an increased risk of heart disease. For example, if one hypothesized that smoking increased the risk of heart attacks, the subjects in the cohort could be sorted based on their smoking habits, and one could compare the subset of the cohort that smoked to the subset who had never smoked. For each such comparison that one wanted to make, the cohort could be grouped according to whether they had a given exposure or not, and one could measure and compare the frequency of heart attacks [i.e., the cumulative incidence or the incidence rates] between the groups.
The Population "At Risk"
From the discussion above, it should be obvious that one of the basic requirements of a cohort type study is that none of the subjects have the outcome of interest at the beginning of the follow-up period, and time must pass in order to determine the frequency of developing the outcome.
For example, if one wanted to compare the risk of developing uterine cancer between postmenopausal women receiving hormone-replacement therapy and those not receiving hormones, one would consider certain eligibility criteria for the members prior to the start of the study: 1] they should be female, 2] they should be post-menopausal, and 3] they should have a uterus. Among post-menopausal women there might be a number who had had a hysterectomy already, perhaps for persistent bleeding problems or endometriosis or prior uterine cancer. Since these women no longer have a uterus, one would want to exclude them from the cohort, because they are no longer at risk of developing this particular type of cancer. Similarly, if one wanted to compare the risk of developing diabetes among nursing home residents who exercised and those who did not, it would be important to test the subjects for diabetes at the beginning of the follow-up period in order to exclude all subjects who already had diabetes and therefore were not "at risk" of developing diabetes.
Prospective Cohort Studies
Cohort studies can be classified as prospective or retrospective based on when outcomes occurred in relation to the enrollment of the cohort. The Framingham Heart Study is an example of a prospective cohort study. Another well-known prospective cohort study is the Nurses' Health Study. The original Nurses' Health Study [NHS] began in 1976 by enrolling about 121,000 female nurses from across the United States who were initially free of known cardiovascular disease or cancer. [The Nurses' Health Study is now enrolling the third generation cohort, which includes male and female nurses].
In a prospective study like the Nurses Health Study baseline information is collected from all subjects in the same way using exactly the same questions and data collection methods for all subjects. The investigators design the questions and data collection procedures carefully in order to obtain accurate information about exposures before disease develops in any of the subjects.
The distinguishing feature of a prospective cohort study is that, at the time that the investigators begin enrolling subjects and collecting baseline exposure information, none of the subjects has developed any of the outcomes of interest.
After baseline information is collected, the participants are followed "longitudinally," i.e. over a period of time, usually for years, to determine if and when they become diseased and whether their exposure status changes. Most studies of this type contact the participants periodically, perhaps every two years, to update information on exposures and outcomes. In this way, investigators can eventually use the data to answer many questions about the associations between exposures ["risk factors"] and disease outcomes. For example, one NHS study examined the association between smoking and breast cancer and found that there was no significant association.
Another NHS study examined the association between obesity and myocardial infarction. They used reported height and weight to calculate BMII and categorized women into five categories of BMI. The table below summarizes their findings with respect to non-fatal myocardial infarction.
| BMI | # non-fatal MIs | Person-Years | Inc. Rate Per 10,000 P-Y | Rate Ratio |
| >=30 | 85 | 99,573 | 85.4 | 3.7 |
| 25.0-29.9 | 67 | 148,541 | 45.1 | 1.6 |
| 23.0-24.9 | 56 | 155,717 | 36.0 | 1.6 |
| 20.0-22.9 | 57 | 194,243 | 29.3 | 1.3 |
| tapply[Dubow,DrugExp,mean] # Gives means of Dubowitz score by drug exposure > tapply[Dubow,DrugExp,sd] # Gives the standard deviations of Dubowitz score by drug exposure > tapply[Ppregwt,DrugExp,mean] # Gives the means of pre-pregnancy weight by drug exposure > tapply[Ppregwt,DrugExp,sd] # Gives the standard deviations of pre-pregnancy weight by drug > tapply[Birthwt,DrugExp,t.test] # Gives 95% confidence interval for exposed and unexposed in one output An Alternate Method of Subset AnalysisGetting descriptive statistics by category can also be achieved as follows: > mean[Birthwt[DrugExp==1]];
mean[Birthwt[DrugExp==0]] # means for each exposure group Using the double equal sign [==] basically means "only if DrugExp equals 1". Creating a Dichotomous Variable from a Continuous VariableSuppose my data set has a continuously distributed variable called "birthwgt", which is each child's weight in grams at birth, but I wish to create a new variable that categorizes children as having Low Birth Weight [lowBW], i.e. less than 2500 grams or not. I can do this using the ifelse[] function, which has the following format: > ifelse[, , ] Example: > lowBW 20%Minimal EffectTotal # Subjects | ||||
| Placebo | 188 | 125 | 313 | |
| Anti-inflammatory drug | 223 | 95 | 318 | |
| Glucosamine | 203 | 114 | 317 | |
| Chondroitin | 208 | 110 | 318 | |
| Glucosamine + Chondroitin | 211 | 106 | 317 |
Data from Clegg DO, et al.: Glucosamine, chondroitin sulfate, and the two
in combination for painful knee osteoarthritis. N Engl J Med 354:795, 2006.
Perhaps the most remarkable observation is the response in the group treated with the placebo which had a cumulative incidence of >20% pain relief of 60% [188/313 = 0.60 = 60%]! This is an example of the "placebo effect" in which patients who perceive they are being treated often report subjective improvement, even if the treatment has no effect. Placebos make the perception of treatment similar among groups and provide a reference group that takes into account the placebo effect. Note also that the group treated with glucosamine and chondroitin had only a slightly greater response rate of 67%.
Analysis of Clinical Trial Data
The analysis of clinical trial data is very similar to the previously described analysis of data from a cohort study. The first step is to generate simple descriptive statistics on each of the groups being compared in order to characterize the study population and alert you and your readers to any differences between the groups with respect to other exposures that might cause confounding. If large numbers of subjects have been randomly assigned to the treatment arms, the groups should be comparable. If there are more than minor discrepancies, the investigators need to review the randomization procedures and consider adjusting for confounding by other methods.
The table below shows just a portion of the data from the table of descriptive statistics from the Physicians' Health Study on aspirin.
| Age [years] | 53.2 ± 9.5 | 53.2 ± 9.5 |
| Systolic BP [mm Hg | 126.1 ± 11.3 | 126.1 ± 11.1 |
| Diastolic BP [mm Hg] | 78.8 ± 7.4 | 78.8 ± 7.4 |
| History of hypertension [%] | 13.5 | 13.6 |
| History of high cholesterol [%] | 17.5 | 17.3 |
| Cholesterol level | 212.1 ± 44.2 | 212.0 ± 45.1 |
| History of diabetes [%] | 2.3 | 2.2 |
Note that the two groups were remarkably similar on these and other characteristics, indicating that randomization had been successful.
After generating the descriptive statistics, the next step is to generate crude estimates for the magnitude of association between the primary exposure and the outcomes of interest.
Test Yourself
Summarize these finding in a contingency table and compute the cumulative incidence in each group, the risk ratio, and the risk difference. Then interpret the risk ratio and the risk difference. Complete all of these tasks before comparing your answers to the ones provided in the link below.
Answer
Strengths and Limitations of Clinical Trials
Large randomized clinical trials can provide strong evidence of the true effect of a treatment or intervention, because they provide excellent control of confounding, but they also have some limitations:
Strengths of Intervention Studies [Clinical Trials]
- They provide the best means of minimizing the effect of confounding
- They avoid bias in allocation to exposure groups
- Large randomized clinical trials are the best design for detecting small to moderate effects that may be clinically important
Limitations
- Ethical issues need to be considered
- Risks to subjects versus potential benefits
- Does equipoise exist? Some questions cannot be answered ethically with a clinical trial.
- They are usually time consuming and costly
- Lengthy trials run the risk of loss to follow up [LTF], and if LTF is different for one of the exposure-outcome categories, the measure of association will be biased, just as with prospective cohort studies[see the module on Bias].
- Invariably, some subjects will fail to adhere to the protocol, and non-adherence will cause an underestimated measure of association [see below].
Non-adherence
Ideally, the investigators want to compare exposed subjects to non-exposed in groups that are similar with respect to confounding factors. The true benefit of a new drug will be underestimated if subjects given the active medication fail to take it, causing subjects who were actually not exposed to be mixed in with the exposed subjects who were actually taking the medication. This mixing of the exposure groups dilutes the apparent benefit causing underestimates of association. The same thing occurs if people in the placebo group begin taking the active medication. This occurred in the Physicians' Health Study in which follow up questionnaires estimated that about 15% of the subjects assigned to the aspirin group did not take it, and a similar proportion of subjects in the placebo group used aspirin fairly regularly. This would cause an underestimate of the true benefit. In this case, in which the exposure was preventive with an observed risk ratio = 0.59, the true risk ratio would have been even smaller. In other words, non-adherence caused a "bias toward the null," an underestimate of the true benefit.
Non-compliance can occur due to side effects of the treatment, illness, or loss of interest in the study.
How to Promote Adherence in a Clinical Trial
- Begin with an interested group of participants
- Make it easy to participate
- Present a realistic picture of the protocol during informed consent
- Exclude participants with pre-existing conditions that make compliance difficult
- Simplify the protocol as much as possible
- Conduct a run-in period if necessary
- Use blinding and placebos
- Keep in
touch
- Maintain frequent contact with subjects WITHOUT interfering with treatment
- Provide incentives [free check-ups, transportation, t-shirts, birthday cards]
Data Safety and Monitoring Board
All clinical trials that involve more than minimal risk are required to have a Data Safety and Monitoring Board [DSMB], which is an independent board of experts not involved in the study who periodically review the data in a trial to evaluate safety, study conduct, and interim results. They can recommend that the study be continued, modified, or terminated. The DSMB for the Physicians' Health Study recommended that the study be terminated after five years because the benefits of taking low-dose aspirin were so clear that continuing to withhold aspirin from the placebo group was not ethically justified. The DSMB felt that equipoise no longer existed.
Intention-to-Treat Analysis versus Efficacy Analysis
The greatest advantage of large randomized clinical trials is that they provide control of confounding. However, as already noted there can be problems due to loss to follow up and lack of adherence to the protocol. It might be tempting to limit the analysis to subjects who completed the study and who adhered to the study protocol, but this efficacy analysis may not provide strong control of confounding, because subjects have, in essence, self-selected whether they would remain in the study and adhere to the protocol. For this reason, well-done clinical trials will conduct and report the results of an intention-to-treat analysis in which subjects are included in the analysis in the groups to which they were randomly assigned regardless of whether they adhered to the protocol. We already noted that non-adherence will bias the results toward the null, i.e., underestimate the association if there is one. However, the intention-to-treat analysis provides the best opportunity to examine the association in the absence of confounding. Many reports will provide the results of the intention-to-treat analysis and the efficacy analysis as well, and they may also analyze sub-groups of subjects, but these analyses need to use other methods to minimize the effects of confounding.