Abstract
Epidemiologists spend a great deal of time on confounding in our teaching, in our methods development, and in our assessment of study results. This may give the impression that uncontrolled confounding is the biggest problem observational epidemiology faces, when in fact, other sources of bias such as selection bias, measurement error, missing data, and misalignment of zero time may often (especially if they are all present in a single study) lead to a stronger deviation from the truth. Compared with the amount of time we spend teaching how to address confounding in data analysis, we spend relatively little time teaching methods for simulating confounding (and other sources of bias) to learn their impact and develop plans to mitigate or quantify the bias. Here we review the accompanying paper by Desai et al (Am J Epidemiol. 2024;193(11):1600-1608), which uses simulation methods to quantify the impact of an unmeasured confounder when it is completely missing or when a proxy of the confounder is measured. We discuss how we can use simulations of sources of bias to ensure that we generate better and more valid study estimates, and we discuss the importance of simulating realistic datasets with plausible bias structures to guide data collection. This article is part of a Special Collection on Pharmacoepidemiology.