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Biomedical studies that use electronic health records (EHR) data forinference are often subject to bias due to measurement error. The measurementerror present in EHR data is typically complex, consisting of errors of unknownfunctional form in covariates and the outcome, which can be dependent. Toaddress the bias resulting from such errors, generalized raking has recentlybeen proposed as a robust method that yields consistent estimates without theneed to model the error structure. We provide rationale for why thesepreviously proposed raking estimators can be expected to be inefficient infailure-time outcome settings involving misclassification of the eventindicator. We propose raking estimators that utilize multiple imputation, toimpute either the target variables or auxiliary variables, to improve theefficiency. We also consider outcome-dependent sampling designs and investigatetheir impact on the efficiency of the raking estimators, either with or withoutmultiple imputation. We present an extensive numerical study to examine theperformance of the proposed estimators across various measurement errorsettings. We then apply the proposed methods to our motivating setting, inwhich we seek to analyze HIV outcomes in an observational cohort withelectronic health records data from the Vanderbilt Comprehensive Care Clinic.

Improved Generalized Raking Estimators to Address Dependent Covariate and Failure-Time Outcome Error