Differential recall bias in estimating treatment effects in observational studies

TL;DR

This paper addresses differential recall bias in observational studies, proposing bounds, estimation methods, and sensitivity analysis techniques to improve causal effect estimation when exposure recall is biased.

Contribution

It introduces novel bounds, multiple estimation strategies, and a sensitivity analysis approach for causal inference under differential recall bias without requiring validation data.

Findings

Methods perform well in simulation under model misspecification

Application reveals potential bias in childhood abuse and mental health study

Sensitivity analysis helps assess robustness of causal conclusions

Abstract

Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassification is recall bias, which occurs in retrospective cohort studies when study subjects may inaccurately recall their past exposure. Particularly challenging is differential recall bias in the context of self-reported binary exposures, where the bias may be directional rather than random , and its extent varies according to the outcomes experienced. This paper makes several contributions: (1) it establishes bounds for the average treatment effect (ATE) even when a validation study is not available; (2) it proposes multiple estimation methods across various strategies predicated on different assumptions; and (3) it suggests a sensitivity analysis technique to assess the robustness of the causal conclusion, incorporating insights from prior research. The effectiveness of these methods is demonstrated through simulation studies that explore various model misspecification scenarios. These approaches are then applied to investigate the effect of childhood physical abuse on mental health in adulthood.