|
Activity Number:
|
90
- Dealing with Error-Prone Electronic Health Record Data via Validation Sampling
|
|
Type:
|
Invited
|
|
Date/Time:
|
Monday, August 8, 2022 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
|
Abstract #320610
|
|
|
Title:
|
SAT: A Surrogate-Assisted Two-Wave Case Boosting Sampling Method, with Application to EHR-Based Association Studies
|
|
Author(s):
|
Yong Chen* and Rebecca Hubbard and Xiaokang Liu and Jessica Chubak
|
|
Companies:
|
University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and Kaiser Permanente Washington Health Research Institute
|
|
Keywords:
|
Association study;
Electronic health records;
Error in phenotype;
Rare disease;
Sampling strategy
|
|
Abstract:
|
Electronic health records (EHR) enable investigation of the association between phenotypes and risk factors. However, studies solely relying on potentially error-prone EHR-derived phenotypes (i.e., surrogates) are subject to bias. Analyses of low prevalence phenotypes may also suffer from poor efficiency. Existing methods typically focus on one of these issues but seldom address both. This study aims to simultaneously address both issues by developing new sampling methods to select an optimal subsample to collect gold standard phenotypes for improving the accuracy of association estimation. We develop a surrogate assisted two-wave (SAT) sampling method, where a surrogate-guided sampling procedure (SGS) and a modified optimal subsampling procedure motivated from A-optimality criterion (OSMAC) are employed sequentially, to select a subsample for outcome validation through manual chart review subject to budget constraints. A model is then fitted based on the subsample with the true phenotypes. Simulation studies and an application to an EHR dataset of breast cancer survivors are conducted to demonstrate the effectiveness of SAT.
|
Authors who are presenting talks have a * after their name.
