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

ABSTRACT: 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 s

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8311974 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature