Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification
| العنوان: | Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification |
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| المؤلفون: | Bhramar Mukherjee, Lauren J. Beesley |
| المصدر: | Biometrics. 78:214-226 |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Statistics and Probability, Michigan, Computer science, media_common.quotation_subject, Survey sampling, Inference, Machine learning, computer.software_genre, Representativeness heuristic, 01 natural sciences, General Biochemistry, Genetics and Molecular Biology, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Bias, Statistical inference, Electronic Health Records, Humans, Leverage (statistics), 030212 general & internal medicine, 0101 mathematics, Selection Bias, 030304 developmental biology, media_common, Selection bias, Likelihood Functions, 0303 health sciences, General Immunology and Microbiology, business.industry, Applied Mathematics, Inverse probability weighting, Estimator, General Medicine, Artificial intelligence, General Agricultural and Biological Sciences, business, computer, Type I and type II errors |
| الوصف: | Health research using electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error. In this paper, we develop new strategies for handling disease status misclassification and selection bias in EHR-based association studies. We first focus on each type of bias separately. For misclassification, we propose three novel likelihood-based bias correction strategies. A distinguishing feature of the EHR setting is that misclassification may berelated to patient-specific factors, and the proposed methods leverage data in the EHR to estimate misclassification rateswithout gold standard labels. For addressing selection bias, we describe how calibration and inverse probability weighting methods from the survey sampling literature can be extended and applied to the EHR setting.Addressing misclassification and selection biases simultaneously is a more challenging problem than dealing with each on its own, and we propose several new strategies to address this situation. For all methods proposed, we derive valid standard errors and provide software for implementation. We provide a new suite of statistical estimation and inference strategies for addressing misclassification and selection bias simultaneously that is tailored to problems arising in EHR data analysis. We apply these methods to data from The Michigan Genomics Initiative (MGI), a longitudinal EHR-linked biorepository. |
| تدمد: | 1541-0420 0006-341X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e099115cd6cef01526f99430974dc6bcTest https://doi.org/10.1111/biom.13400Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e099115cd6cef01526f99430974dc6bc |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15410420 0006341X |
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