Bias reduction methods for propensity scores estimated from error-prone EHR-derived covariates
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Bias reduction methods for propensity scores estimated from error‑prone EHR‑derived covariates Joanna Harton1 · Ronac Mamtani2 · Nandita Mitra1 · Rebecca A. Hubbard1 Received: 15 January 2020 / Revised: 17 July 2020 / Accepted: 24 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract As the use of electronic health records (EHR) to estimate treatment effects has become widespread, concern about bias introduced by error in EHR-derived covariates has also grown. While methods exist to address measurement error in individual covariates, little prior research has investigated the implications of using propensity scores for confounder control when the propensity scores are constructed from a combination of accurate and error-prone covariates. We reviewed approaches to account for error in propensity scores and used simulation studies to compare their performance. These comparisons were conducted across a range of scenarios featuring variation in outcome type, validation sample size, main sample size, strength of confounding, and structure of the error in the mismeasured covariate. We then applied these approaches to a real-world EHR-based comparative effectiveness study of alternative treatments for metastatic bladder cancer. This head-to-head comparison of measurement error correction methods in the context of a propensity score-adjusted analysis demonstrated that multiple imputation for propensity scores performs best when the outcome is continuous and regression calibration-based methods perform best when the outcome is binary. Keywords Electronic health record (EHR) data · Missingness · Bias · Mismeasurement · Regression calibration · Propensity score
1 Introduction Data from Electronic Health Records (EHR) have the potential to facilitate research on exposures and outcomes that would be difficult to study using designed observational or experimental studies due to feasibility or ethical considerations. However, because EHR Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1074 2-020-00219-3) contains supplementary material, which is available to authorized users. Nandita Mitra and Rebecca Hubbard: Co-senior authors. * Joanna Harton [email protected] 1
Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Health Services and Outcomes Research Methodology
were developed for clinical care and administrative purposes, the data elements needed for research are often incomplete or error-prone. Many recent studies have highlighted data quality challenges arising in EHR-based research (Hersh et al. 2013; Weiskopf and Weng 2013; Rusanov et al. 2014). Statistical methods to account for missing and error-prone data are needed in order for valid research results to be obtained from the available EHR. Without appropriate analytic approaches treatment effect estima
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