Before concluding that an individual study's conclusions are valid, one must consider three sources of error that might provide an alternative explanation for the findings. These are:
- Random Error
If a determination is made that the findings of a study were not due to any one of these three sources of error, then the study is considered internally valid. In other words, the conclusions reached are likely to be correct for the circumstances of that particular study. This does not not necessarily mean that the findings can be generalized to other circumstances (external validity). For example, the Physicians' Health Study concluded that aspirin use reduced the risk of myocardial infarction in adult male physicians in the United States. The study was carefully done, and the study was internally valid, but it was not clear that the results could be extrapolated to women, or even to non-physicians (whose risk of myocardial infarction is generally lower than that of the population overall). However, internally validity must be established before one can consider whether the results are externally valid. Therefore, investigators must first ensure that a study is internally valid, even if that means that the generalizability of the findings will be compromised.
In contrast to random error, bias refers to systematic errors in any type of epidemiologic study that result in an incorrect estimate of the association between exposures and outcomes. Investigators can introduce bias into a study as a result of the procedures for identifying and enrolling subjects or from the procedures for collecting or analyzing information. Bias can also be introduced by errors in classification of outcomes or exposures. It is important for investigators to be mindful of potential biases in order to reduce their likelihood when they are designing a study, because once bias has been introduced, it cannot be removed. The two major types of bias are:
- Selection Bias
- Information Bias
In addition, many epidemiologists think of confounding as a type of bias. While confounding also produces incorrect estimates of the association, one can often adjust for confounding in the analysis in order to remove its distorting effects to obtain a more accurate measure of association. The problem of confounding will be addressed in a separate module.
When reading a study, one should be aware of potential biases that might have affected the conclusions and be able to predict what effect a given bias would be expected to have on the estimate of effect.
After successfully completing this unit, the student will be able to:
- Define bias (systematic error) and differentiate between the three types of bias: selection bias, misclassification/information bias, and confounding bias
- Define and identify selection bias
- Define and identify non-differential misclassification of disease and exposure
- Define and identify differential misclassification of exposure in a case-control study (recall bias and interviewer bias)
- Identify the effect that a particular bias can have on a particular study
- Identify which types of studies are prone to which types of bias
- Identify common sources of bias