Exposure measurement error correction in longitudinal studies with discrete outcomes

2025-05-22

AI Summary

In longitudinal studies, time-varying exposure history functions often induce bias in effect estimation for discrete health outcomes due to measurement error in individual-level exposure assessments. This paper introduces the first measurement error correction framework for exposure history functions in longitudinal designs with discrete outcomes, specifically tailored for nested validation study designs. The method integrates validation data modeling, simulation-extrapolation (SIMEX), and multiple correction strategies—including regression calibration—within a generalized linear mixed model framework. Applied to the association between long-term PM₂.₅ exposure and anxiety disorders, the corrected estimates exhibit improved robustness. Simulation studies demonstrate over 70% reduction in bias and 95% confidence interval coverage approaching the nominal level. This framework substantially enhances estimator unbiasedness and statistical inference reliability, particularly under limited sample sizes.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Correcting exposure measurement errors in longitudinal studies
• Estimating effects of time-varying exposure on health outcomes
• Reducing bias in discrete outcome analyses with mismeasured data

Innovation

• Corrects exposure measurement errors in longitudinal studies
• Uses main study and validation study design
• Improves bias reduction and coverage probability