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

TL;DR

This paper introduces improved generalized raking estimators that incorporate multiple imputation and account for dependent measurement errors in covariates and failure-time outcomes, enhancing bias correction and efficiency in EHR-based biomedical studies.

Contribution

It develops novel raking estimators with multiple imputation for failure-time data, addressing dependent measurement errors and outcome-dependent sampling, which were not adequately handled before.

Findings

Proposed estimators outperform existing methods in simulation studies.

Application to HIV EHR data demonstrates practical utility.

Enhanced efficiency and bias reduction in complex measurement error settings.

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 variables, to improve the efficiency. We also consider outcome-dependent sampling designs and investigate their impact on the efficiency of the raking estimators, either with or without multiple imputation. We present an extensive numerical study to examine the performance of the proposed estimators across various measurement error settings. We then apply the proposed methods to our motivating setting, in which we seek to analyze HIV outcomes in an observational cohort with electronic health records data from the Vanderbilt Comprehensive Care Clinic.