Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies

Pharmacoepidemiology has an increasingly important role in informing and improving clinical practice, drug regulation, and health policy. Therefore, unrecognized biases in pharmacoepidemiologic studies can have major implications when study findings are translated to the real world. We propose a simple taxonomy for researchers to use as a starting point when thinking through some of the most pervasive biases in pharmacoepidemiology. We organize this discussion according to biases best assessed with respect to the study population (including confounding by indication, channeling bias, healthy user bias, and protopathic bias), the study design (including prevalent user bias and immortal time bias), and the data source (including misclassification bias and missing data/loss to follow up). This tutorial defines, provides a curated list of recommended references, and illustrates through relevant case examples these key biases to consider when planning, conducting, or evaluating pharmacoepidemiologic studies.

Automated summary

Pharmacoepidemiology plays an increasingly important role in informing clinical practice, drug regulation, and health policy. This article proposes a simple taxonomy to help researchers identify the most common biases in pharmacoepidemiology studies. By considering biases related to the study population, study design, and data source, researchers can plan, conduct, and evaluate pharmacoepidemiologic studies more effectively.