Raking and regression calibration: Methods to address bias from correlated covariate and time-to-event error.

ABSTRACT: Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed; however, time-to-event error has also been shown to cause significant bias, but methods to address it are relatively underdeveloped. More generally, it is possible to observe errors in both the covariate and the time-to-event outcome that are correlated. We propose regression calibration (RC) estimators to simultaneously address correlated error in the covariates and the censored event time. Al

SUBMITTER: Oh EJ 

PROVIDER: S-EPMC7874496 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature