Improved generalized raking estimators to address dependent covariate and failure-time outcome error.

ABSTRACT: Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typically complex, consisting of errors of unknown functional form in covariates and the outcome, which can be dependent. To address the bias resulting from such errors, generalized raking has recently been proposed as a robust method that yields consistent estimates without the need to model the error structure. We provide rationale for why these previously proposed raking estimators can be expected to be inefficient in failure-time outcome settings involving misclassification of the event indicator. We propose raking estimators that utilize multiple imputation, to impute either the target variables or auxiliary var

SUBMITTER: Oh EJ 

PROVIDER: S-EPMC8211389 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature