ArticleJournal of the American Medical Informatics Association : JAMIA2024
To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Article
- Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Assessing the generalizability of prevalence estimates from the All of Us Research Program.American journal of epidemiology · 2026Article
- Deviation From Genetically Predicted BMI and All-Cause Mortality: A Cohort Study in the UK Biobank.Obesity (Silver Spring, Md.) · 2026Article
- Robust replication of associations across patient-mediated and provider-sourced EHR data in thenpj digital public health · 2026Article
- What's the Weight? Estimating Controlled Outcome Differences in Complex Surveys for Health Disparities Research.Statistics in medicine · 2025Article
- Sociodemographic, health-related, and clinical characteristics and their associations with mortality among All of Us participants compared with the United States general population.American journal of epidemiology · 2025Article
- Revisiting representativeness.International journal of epidemiology · 2025Article
- Explainable machine learning for health disparities: type 2 diabetes in thebioRxiv : the preprint server for biology · 2025Article
- Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample Weighting.medRxiv : the preprint server for health sciences · 2024Article
- Addressing methodological and logistical challenges of using electronic health record (EHR) data for research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
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10 authors.
Funding
Abstract
objectivesTo develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. MATERIALS AND
methodsWe mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results.
resultsFor AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. DISCUSSION: Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis.
conclusionEHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.
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