Semiparametric Methods for Exposure Misclassification in Propensity Score-Based Time-to-Event Data Analysis
This addresses a challenging issue in epidemiology for researchers analyzing time-to-event outcomes with misclassified exposures, though it is incremental as it builds on existing propensity score methods.
The paper tackles the problem of exposure misclassification bias in propensity score-based Cox regression for time-to-event data, proposing an estimating equation method that corrects this bias, as demonstrated in a simulation study and applied to estimate the association of PM2.5 with lung cancer mortality in the Nurses' Health Study.
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 (NHS). The proposed methodology can be applied using our user-friendly R function published online.