ArticleJournal of the Royal Statistical Society. Series A, (Statistics in Society)2024
A framework for understanding selection bias in real-world healthcare data.
Article in Journal of the Royal Statistical Society. Series A, (Statistics in Society), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks.PLoS genetics · 2023Pooled it
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- Surgery-first versus neoadjuvant chemotherapy in stage I (cT1c) triple-negative breast cancer: A critical reappraisal.Breast (Edinburgh, Scotland) · 2026Review
- Real-world treatment patterns and clinical outcomes of patients with primary biliary cholangitis in the United States.Journal of comparative effectiveness research · 2026Article
- Privacy-enhancing sequential learning under heterogeneous selection bias in multi-site electronic health records data.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Comment on 'A Prospective Evaluation of Glucagon Stimulation Test Safety in Adults With Chronic Moderate-to-Severe Traumatic Brain Injury'.Endocrinology, diabetes & metabolism · 2026Article
- Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values.JCO clinical cancer informatics · 2026Article
- The SCCAI and Mayo Endoscopic Severity Scores Are Positively Correlated with QIDS-SR16 Depression Scores in Patients with Ulcerative Colitis.Psychology research and behavior management · 2026Article
- Teaching common cognitive biases and associated errors in veterinary neurologic diagnosis using clinical vignettes: a narrative review.Frontiers in veterinary science · 2026Review
- Public Trust, Private Data: Four Considerations for the Future of Health.American journal of 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
- EBUS-TBNA for Diagnosis and Staging of Lung Cancer: A Retrospective Regional Analysis Integrating Clinical and Molecular Data (EXPoSURE Score).Journal of clinical medicine · 2025Article
- Revisiting representativeness.International journal of epidemiology · 2025Article
- Transparency in the secondary use of health data: assessing the status quo of guidance and best practices.Royal Society open science · 2025Article
- To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Exploring the Big Data Paradox for various estimands using vaccination data from the global COVID-19 Trends and Impact Survey (CTIS).Science advances · 2024Article
- To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Using administrative patient-care data such as Electronic Health Records (EHR) and medical/pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real-world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.