Accounting for recall bias in case-control studies: a causal inference approach

TL;DR

This paper addresses recall bias in case-control studies by defining the causal odds ratio, developing estimation methods accounting for recall bias, and introducing the R-factor to assess bias impact, demonstrated through a study on childhood abuse.

Contribution

It introduces a causal inference framework for quantifying and adjusting for recall bias in case-control studies, including the novel R-factor metric.

Findings

Recall bias can significantly distort causal odds ratio estimates.

The R-factor quantifies the minimal bias needed to change study conclusions.

Application to childhood abuse data illustrates the framework's practical utility.

Abstract

A case-control study is designed to help determine if an exposure is associated with an outcome. However, since case-control studies are retrospective, they are often subject to recall bias. Recall bias can occur when study subjects do not remember previous events accurately. In this paper, we first define the estimand of interest: the causal odds ratio (COR) for a case-control study. Second, we develop estimation approaches for the COR and present estimates as a function of recall bias. Third, we define a new quantity called the \textit{R-factor}, which denotes the minimal amount of recall bias that leads to altering the initial conclusion. We show that a failure to account for recall bias can significantly bias estimation of the COR. Finally, we apply the proposed framework to a case-control study of the causal effect of childhood physical abuse on adulthood mental health.