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Activity Number: 407 - Data Science Applications in Epidemiology
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #324015 View Presentation
Title: A Novel Framework for Selection Bias in Electronic Health Records-Based Research
Author(s): Sarah B Peskoe* and Sebastien Haneuse
Companies: Harvard TH Chan School of Public Health and TE-Harvard T.H. Chan School of Public Health
Keywords: Electronic Health Record (EHR) ; Inverse Probability Weighting (IPW) ; Comparative Effectiveness Research (CER) ; Missing Data ; Selection Bias ; Bias-Variance Trade-Off
Abstract:

Electronic Health Records (EHR) data provide unique opportunities for comparative effectiveness research (CER) as a result of their rich collection of information for large patient populations. Selection due to incomplete data is an underappreciated source of bias in analyzing EHR data. When framed as a missing-data problem, standard methods are often applied to control for selection bias. In EHR-based studies, however, data provenance involves the interplay of many clinical decisions made by patients, health care providers, and the health system; thus standard methods fail to capture the complexity of the data. In this paper, we use a novel framework for selection bias in EHR-based CER that allows for a hierarchy of missingness mechanisms to inform an inverse-probability weighted estimator that better aligns with the complex nature of EHR data. We show that this estimator is consistent and asymptotically normal. Based off extensive simulations, a key insight is the bias-variance trade-off in using this framework when the data provenance is functionally misspecified. We use this approach to adjust for selection in an on-going, multi-site EHR-based study of bariatric surgery on BMI.


Authors who are presenting talks have a * after their name.

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