Exposure misclassification in propensity score-based time-to-event data analysis

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses' Health Study. The proposed methodology can be applied using our user-friendly R program published online.

Automated summary

In epidemiology, incorporating propensity score in the Cox regression model can effectively control for confounding, but determining exposure effect using propensity score remains challenging in situations with moderate to substantial error in exposure measurement. This paper proposes an estimating equation method to correct bias caused by exposure misclassification, providing more accurate estimation of exposure-outcome associations. Simulation studies are conducted to evaluate the performance of the proposed estimators in various settings, with an application to correct bias in estimating the association of PM2.5 levels with lung cancer mortality.