Exposure Measurement Error Correction in Longitudinal Studies With Discrete Outcomes

• Published in: Statistics in medicine Vol. 44; no. 15-17; pp. e70191 - n/a
• Main Authors: Yang, Ce, Zhang, Ning, Li, Jiaxuan, Mehta, Unnati V., Hart, Jaime E., Spiegelman, Donna L., Wang, Molin
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.07.2025; Wiley Subscription Services, Inc
• Subjects: air pollution; anxiety; Anxiety Disorders - epidemiology; Anxiety Disorders - etiology; Bias; Computer Simulation; Environmental Exposure - adverse effects; Environmental Exposure - analysis; Environmental Exposure - statistics & numerical data; generalized estimating equation; Humans; longitudinal data; Longitudinal Studies; measurement error correction; Models, Statistical; Particulate Matter - adverse effects; anxiety; air pollution; generalized estimating equation; longitudinal data; measurement error correction
• DOI: 10.1002/sim.70191
• ISSN: 0277-6715; 1097-0258

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$$ {\mathrm{PM}}_{2.5} $$, 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$$ {\mathrm{PM}}_{2.5} $$.