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Observational studies are frequently used to estimate the effect of anexposure or treatment on an outcome. To obtain an unbiased estimate of thetreatment effect, it is crucial to measure the exposure accurately. A commontype of exposure misclassification is recall bias, which occurs inretrospective cohort studies when study subjects may inaccurately recall theirpast exposure. Specifically, differential recall bias can be problematic whenexamining the effect of a self-reported binary exposure since the magnitude ofrecall bias can differ between groups. In this paper, we provide the followingcontributions: 1) we derive bounds for the average treatment effect (ATE) inthe presence of recall bias; 2) we develop several estimation approaches underdifferent identification strategies; 3) we conduct simulation studies toevaluate their performance under several scenarios of model misspecification;4) we propose a sensitivity analysis method that can examine the robustness ofour results with respect to different assumptions; and 5) we apply the proposedframework to an observational study, estimating the effect of childhoodphysical abuse on adulthood mental health.

Differential recall bias in estimating treatment effects in observational studies