Measurement Error Correction for Spatially Defined Environmental Exposures in Survival Analysis

TL;DR

This paper introduces a new measurement error correction method for spatial environmental exposures in survival analysis, improving accuracy over traditional static buffer approaches by incorporating dimension reduction and external validation data.

Contribution

It develops a novel correction technique using principal component analysis and external GPS data to address measurement error in spatial exposure assessments within Cox models.

Findings

Improves accuracy of exposure effect estimates in simulations

Demonstrates significant bias reduction in real data application

Enhances understanding of environmental impact on health outcomes

Abstract

Environmental exposures are often defined using buffer zones around geocoded home addresses, but these static boundaries can miss dynamic daily activity patterns, leading to biased results. This paper presents a novel measurement error correction method for spatially defined environmental exposures within a survival analysis framework using the Cox proportional hazards model. The method corrects high-dimensional surrogate exposures from geocoded residential data at multiple buffer radii by applying principal component analysis for dimension reduction and leveraging external GPS-tracked validation datasets containing true exposure measurements. It also derives the asymptotic properties and variances of the proposed estimators. Extensive simulations are conducted to evaluate the performance of the proposed estimators, demonstrating its ability to improve accuracy in estimated exposure effects. An illustrative application assesses the impact of greenness exposure on depression incidence in the Nurses' Health Study (NHS). The results demonstrate that correcting for measurement error significantly enhances the accuracy of exposure estimates. This method offers a critical advancement for accurately assessing the health impacts of environmental exposures, outperforming traditional static buffer approaches.