Exposure Measurement Error Correction in Longitudinal Studies With Discrete Outcomes

TL;DR

This paper introduces a new method to correct exposure measurement errors in longitudinal studies with discrete outcomes, improving the accuracy of health effect estimates.

Contribution

A novel method is developed to reduce bias in estimating exposure effects with discrete outcomes in longitudinal studies.

Findings

The proposed method reduces finite sample bias and improves coverage probability in simulation studies.

Failure to correct exposure errors leads to underestimation of chronic exposure effects, as shown in the PM2.5 and anxiety study.

Abstract

Environmental epidemiologists are often interested in estimating the effect of time‐varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main study/validation study design is developed. Various estimation procedures are explored. Simulation studies were conducted to assess its performance compared to standard analysis, and we found that the proposed method had good performance in terms of finite sample bias reduction and nominal coverage probability improvement. As an illustrative example, we applied the new method to a study of long‐term exposure to PM2.5, in relation to the occurrence of anxiety disorders in the Nurses' Health Study II. Failing to correct the error‐prone exposure can lead to an underestimation of the chronic exposure effect of PM2.5.