Prevalent User Bias in Cross-sectional and Longitudinal Studies: A Concept Simply Explained

Prevalent user bias, such as in the context of treatment with medication, occurs when a medication-using sample is attenuated by the experience of medication use, separating the original sample into former users and continuing users ("prevalent users"). Recruiting a sample after such a change has occurred results in a biased sample. This is problematic when the biasing influence is relevant to the outcome being studied. Three hypothetical studies are described to illustrate prevalent user bias: a cross-sectional study, a longitudinal observational study, and a randomized controlled trial (RCT). Concepts related to prevalent user bias are discussed. For example, this bias may help explain the well-known obesity paradox; and, performing completer analyses in RCTs is fallacious because it is an examination of outcomes in "prevalent users." Prevalent user bias can be avoided by recruiting only new users. If a study recruits "prevalent users," contamination by prevalent user bias should be considered. Finally, in longitudinal studies, reasons for drop out should be ascertained to determine whether the reasons influence outcomes through the prevalent user bias.