It can be more difficult to isolate a biological effect of a treatment if the investigator uses broadly-defined cohorts, i.e., patients with a variety of disease types and/or severity. It is easier to isolate the biological effect of a treatment in a narrowly-defined cohort because of patient homogeneity. The researcher's job is to balance these factors. Every situation is different and the researcher needs to think carefully when defining the selection criteria.
Although a narrowly-defined cohort may have some external validity for others with the same disease if the treatment appears to be beneficial, in general, it will lack external validity because the study results may not apply to patients with slightly altered versions of the disease. Again, these are examples of the competing demands that the researcher must keep in mind.
Epidemiologists have defined the “healthy worker effect” as the phenomenon that the general health of employed individuals is better than average. For example, employed individuals may be unsuitable controls for a case-control study if the cases are hospitalized, patients. Similarly, individuals who volunteer for clinical trials may have more favorable outcomes than those who refuse to participate, even if the treatment is ineffective. This selection effect is known as the “trial participant effect” and it can be strong. For a randomized trial, however, this may not be a problem unless selection effects somehow impact treatment assignment.
Because of the possible effects of prognostic (variables that can affect the outcome) and selection factors on differences in outcome, the eligibility criteria for the study cohort need to be defined carefully.
Two contrasting philosophies in defining these criteria are as follows:
- Define very narrow eligibility criteria so that the study cohort is relatively homogeneous, which may yield an outcome variable that has less variability and result in a smaller sample size; however, the results may not have external validity.
- Define very broad eligibility criteria, and accommodate the larger amount of variability by incorporating a larger sample size, which will provide much more external validity. (This is easy for a statistician to say!)
In many instances, endpoints/outcomes are more easily evaluated if certain complicating factors are prevented by patient exclusions. For example, habitual smokers typically are excluded from asthma trials because their lung function may be impaired by smoking as well as by their asthma. The smoking behavior may confound the results of the study. Exclusions also may be invoked for ethical reasons if the treatment is not expected to benefit a certain subgroup of patients. For example, some oncology (cancer) trials might exclude patients whose life expectancy does not exceed six months.
The difficulties with interpretation of inclusion and exclusion criteria can be minimized via quantitative expressions. For example, inclusion criteria should specify the range of allowable serum chemistry variables, instead of just stating that "we will require normal lab values". Different hospitals are going to have different interpretations of what “normal” is. Obviously, you need to be specific.
Once the decisions are made about the study cohort and other design issues resolved, the protocol approved and study medications obtained, the investigator begins what can be the most difficult task in a clinical trial - recruitment! Despite the most optimistic beliefs about the existence of available patients out there, a host of factors can make the recruitment of patients challenging.
(You may notice in this section we have defined a study ‘cohort’ for the trial. This doesn’t mean however that every clinical trial is a cohort study in the sense of a long-term study following a defined group of patients.)