ReviewPharmacoepidemiology and drug safety2022
Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.
Review in Pharmacoepidemiology and drug safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How much can we save by applying artificial intelligence in evidence synthesis? Results from a pragmatic review to quantify workload efficiencies and cost savings.Frontiers in pharmacology · 2025Pooled it
- Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.BMC medical research methodology · 2026Article
- An operational target trial emulation framework for causal inference using electronic health record data.NPJ digital medicine · 2026Review
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
- Article
- High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome.Pharmacoepidemiology and drug safety · 2025Article
- Efficacy and safety ofBMJ open · 2025Article
- How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?Pharmacoepidemiology and drug safety · 2025Article
- Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies.BMC medical research methodology · 2025Article
- Article
- Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer.Proceedings of machine learning research · 2024Article
- Computational Approaches for Connecting Maternal Stress to Preterm Birth.Clinics in perinatology · 2024Review
- Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.Pharmacoepidemiology and drug safety · 2024Article
- Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.Pharmacoepidemiology and drug safety · 2022Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
purposeSupplementing investigator-specified variables with large numbers of empirically identified features that collectively serve as 'proxies' for unspecified or unmeasured factors can often improve confounding control in studies utilizing administrative healthcare databases. Consequently, there has been a recent focus on the development of data-driven methods for high-dimensional proxy confounder adjustment in pharmacoepidemiologic research. In this paper, we survey current approaches and recent advancements for high-dimensional proxy confounder adjustment in healthcare database studies.
methodsWe discuss considerations underpinning three areas for high-dimensional proxy confounder adjustment: (1) feature generation-transforming raw data into covariates (or features) to be used for proxy adjustment; (2) covariate prioritization, selection, and adjustment; and (3) diagnostic assessment. We discuss challenges and avenues of future development within each area.
resultsThere is a large literature on methods for high-dimensional confounder prioritization/selection, but relatively little has been written on best practices for feature generation and diagnostic assessment. Consequently, these areas have particular limitations and challenges.
conclusionsThere is a growing body of evidence showing that machine-learning algorithms for high-dimensional proxy-confounder adjustment can supplement investigator-specified variables to improve confounding control compared to adjustment based on investigator-specified variables alone. However, more research is needed on best practices for feature generation and diagnostic assessment when applying methods for high-dimensional proxy confounder adjustment in pharmacoepidemiologic studies.
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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.