Thursday, April 4, 2019

Thursday, April 4, 2019 — 4:00 PM EDT

Estimation methods to address correlated errors in time-to-event outcomes and exposures

Electronic health records (EHR) data are increasingly used in medical research, but EHR data, which typically are not collected to support research, are often subject to measurement error. These errors, if not addressed, can bias results in association analyses. Methodology to address covariate measurement error has been well developed; however, methods to address errors in time-to-event outcomes are relatively underdeveloped. We will consider methods to address errors in both the covariate and time-to-event outcome that are potentially correlated. We develop an extension to the popular regression calibration method for this setting. Regression calibration has been shown to perform well for settings with covariate measurement error (Prentice, 1982; Shaw and Prentice, 2012), but it is known that this method is generally biased for nonlinear regression models, such as the Cox model for time-to-event outcomes. Thus, we additionally propose raking estimators, which will be unbiased when an unbiased estimating equation is available on a validation subset. Raking is a standard method in survey sampling that makes use of auxiliary information on the population to improve upon the simple Horvitz-Thompson estimator applied to a subset of data (e.g. the validation subset). We demonstrate through numerical studies that raking can improve upon the regression calibration estimators in certain settings with failure-time data. We will discuss the choice of the auxiliary variable and aspects of the underlying estimation problem that affect the degree of improvement that the raking estimator will have over the simpler, biased regression calibration approach. Detailed simulation studies are presented to examine the relative performance of the proposed estimators under varying levels of signal, covariance, and censoring. We further illustrate the methods with an analysis of observational EHR data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.

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