Exposure measurement error correction in longitudinal studies with discrete outcomes

TL;DR

This paper develops a new method to correct measurement errors in exposure data for longitudinal studies with discrete health outcomes, improving bias and coverage in effect estimates.

Contribution

A novel correction method for measurement error in longitudinal discrete outcome studies, validated through simulations and applied to PM2.5 exposure and anxiety disorders.

Findings

Proposed method reduces bias in effect estimates.

Simulation shows improved coverage probability.

Application reveals underestimation of PM2.5 effects without correction.

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.