Three Methods for Minimizing Confounding in the Study Design Phase
Confounding is a major problem in epidemiologic research, and it accounts for many of the discrepancies among published studies. Nevertheless, there are ways of minimizing confounding in the design phase of a study, and there are also methods for adjusting for confounding during analysis of a study.
Randomization in a Clinical Trial
The ideal way to minimize the effects of confounding is to conduct a large randomized clinical trial so that each subject has an equal chance of being assigned to any of the treatment options. If this is done with a sufficiently large number of subjects, other risk factors (i.e., confounding factors) should be equally distributed among the exposure groups. The beauty of this is that even unknown confounding factors will be equally distributed among the comparison groups. If all of these other factors are distributed equally among the groups being compared, they will not distort the association between the treatment being studied and the outcome.
The success of randomization is usually evaluated in one of the first tables in a clinical trial, i.e., a table comparing characteristics of the exposure groups. If the groups have similar distributions of all of the known confounding factors, then randomization was successful. However, if randomization was not successful in producing equal distributions of confounding factors, then methods of adjusting for confounding must be used in the analysis of the data.
Strengths of Randomization
- There is no limit on the number of confounders that can be controlled
- It controls for both known and unknown confounders
- If successful, there is no need to "adjust" for confounding
Limitations of Randomization to Control for Confounding
- It is limited to intervention studies (clinical trials)
- It may not be completely effective for small trials
Restriction of Enrollment
Limiting the study to subjects in one category of the confounder is a simple way of ensuring that all participants have the same level of the confounder. For example,
- If smoking is a confounding factor, one could limit the study population to only non-smokers or only smokers.
- If sex is a confounding factor, limit the participants to only men or only women
- If age is a confounding factor, restrict the study to subjects in a specific age category, e.g., persons >65.
Drawbacks of Restriction
Restriction is simple and generally effective, but it has several drawbacks:
- It can only be used for known confounders and only when the status of potential subjects is known with respect to that variable
- Residual confounding may occur if restriction is not narrow enough. For example, a study of the association between physical activity and heart disease might be restricted to subjects between the ages of 30-60, but that is a wide age range, and the risk of heart disease still varies widely within that range.
- Investigators cannot evaluate the effect of the restricted variable, since it doesn't vary
- Restriction limits the number of potential subjects and may limit sample size
- If restriction is used, one cannot generalize the findings to those who were excluded.
- Restriction is particularly cumbersome if used to control for multiple confounding variables.
Matching Compared Groups
Another risk factor can only cause confounding if it is distributed differently in the groups being compared. Therefore, another method of preventing confounding is to match the subjects with respect to confounding variables. This method can be used in both cohort studies and in case-control studies in order to enroll a reference group that has artificially been created to have the same distribution of a confounding factor as the index group. For example,
- In a case-control study of lung cancer where age is a potential confounding factor, match each case with one or more control subjects of similar age. If this is done the age distribution of the comparison groups will be the same, and there will be no confounding by age.
- In a cohort study on effects of smoking each smoker (the index group) who is enrolled is matched with a non-smoker (reference group) of similar age. Once again, the groups being compared will have the same age distribution, so confounding by age will be prevented
Advantages of Matching
- Matching is particularly useful when trying to control for complex or difficult to measure confounding variables, e.g., matching by neighborhood to control for confounding by air pollution.
- It can also be used in case-control studies with few cases when additional control subjects are enrolled to increase statistical power, e.g., 4 to 1 matching of controls to cases.
Drawbacks of Matching
- It can only be used for known confounders.
- It can be difficult, expensive, and time-consuming to find appropriate matches.
- One cannot evaluate the effect of the matched variable.
- Matching requires special analytic methods.