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A robust approach for electronic health record-based case-control studies with contaminated case pools
Guorong Dai1, Yanyuan Ma2, Jill Hasler3
1Department of Statistics and Data Science, School of Management, Fudan University, Shanghai, China.
Biometrics
|July 16, 2022
Summary
This study introduces a novel method for analyzing electronic health records (EHRs) case-control studies with contaminated case pools. The approach adaptively imputes unknown case status, improving accuracy and efficiency in estimating odds ratios.
Area of Science:
- Biostatistics
- Epidemiology
- Health Informatics
Background:
- Case-control studies using electronic health records (EHRs) often face challenges with ineligible patients misclassified as cases.
- Accurate identification of true cases is crucial for reliable epidemiological analyses.
Purpose of the Study:
- To develop a robust method for estimating odds ratios in case-control studies with contaminated case pools from EHRs.
- To address the challenge of unknown true outcome status in a significant portion of the case pool.
Main Methods:
- Propose a general strategy to adaptively impute unknown case status without a perfect phenotyping model.
- Utilize unbiased estimating equations constructed from all available data to estimate parameters.
- Employ logistic regression models for association analysis.
Main Results:
- The proposed method effectively removes the influence of false cases.
- Achieves robustness to mismodeling of the outcome status-covariate relationship.
- Demonstrates improved estimation efficiency compared to existing methods.
- The estimator is shown to be root-n-consistent and asymptotically normal.
Conclusions:
- The novel imputation strategy offers a robust and statistically efficient solution for analyzing EHR-based case-control studies with contaminated case pools.
- The method shows desirable robustness to potential misspecification in both association and phenotyping models.
- Outperforms existing methods in simulations and real EHR data analysis.
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