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Bias reduction and inference for electronic health record data under selection and phenotype misclassification: three
Lauren J Beesley1, Bhramar Mukherjee1
1University of Michigan, Department of Biostatistics.
Medrxiv : the Preprint Server for Health Sciences
|January 5, 2021
Summary
This study introduces a new framework to address biases in Electronic Health Records (EHR) research. The methods leverage external data to improve accuracy in population health studies.
Area of Science:
- Biostatistics
- Health Informatics
- Epidemiology
Background:
- Electronic Health Records (EHR) offer rich longitudinal patient data but are not optimized for population-based research.
- Existing statistical methods struggle to simultaneously address multiple biases like selection bias and phenotype misclassification in EHR data.
- Challenges remain in understanding and accounting for partially observed factors influencing data biases.
Conclusions:
- The proposed methods effectively utilize auxiliary information to mitigate selection bias and phenotype misclassification in EHR data.
- The case studies demonstrate practical application and the potential for less biased inference in population health research using EHRs.
- The SAMBA framework offers a robust approach for improving the reliability of findings derived from electronic health records.
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