ArticleStatistical methods in medical research2023
An overview of propensity score matching methods for clustered data.
Article in Statistical methods in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Beyond Single-Score Matching: A Two-Dimensional Propensity Score Method for Mixed Covariate Types.Research square · 2026Article
- [Exploring predictors of non-invasive positive pressure ventilation failure in children with severe pneumonia based on propensity score matching].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Article
- Using Propensity Score Weighting With Clustered Data When the Treatment Is Applied at the Level of the Cluster and Outcomes Are Assessed at the Level of the Individual: The Observational Analog of Cluster Randomization Trials.Statistics in medicine · 2026Article
- Aspirin in Audiology: A Dual-Edged Sword in Hearing Loss and Its Nanotechnology-Driven Future.Drug design, development and therapy · 2026Review
- Multidimensional effects of the Xin'an Jianpi Tongbi Formula on self-perception of patients with rheumatoid arthritis: focusing on the mediating role of systemic inflammation index.Frontiers in medicine · 2026Article
- Key dietary amino acids modulating overweight/obesity risk in Chinese children and adolescents: a machine learning analysis of a national survey.Frontiers in nutrition · 2026Article
- Article
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- Inverse probability weighting for causal inference in hierarchical data.BMC medical research methodology · 2025Article
- The New Horizon: A Viewpoint of Novel Drugs, Biomarkers, Artificial Intelligence, and Self-Management in Improving Kidney Transplant Outcomes.Journal of clinical medicine · 2025Article
- Propensity Score Matching: Identifying Opportunities for Future Use in Nursing Studies.Nursing reports (Pavia, Italy) · 2025Article
- TriNetX and Real-World Evidence: A Critical Review of Its Strengths, Limitations, and Bias Considerations in Clinical Research.ASIDE internal medicine · 2025Article
- Unveiling novel therapeutic mechanisms of Xinfeng capsule: modulating the ALKBH5-m6A-LINC00968 axis to alleviate oxidative stress-driven NETosis in rheumatoid arthritis.Frontiers in immunology · 2025Article
- Novel modified blumgart anastomosis reduces clinically relevant pancreatic fistula after pancreaticoduodenectomy: a retrospective study using inverse probability of treatment weighting.Frontiers in surgery · 2025Article
- Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.Frontiers in public health · 2025Article
- Evaluating Bias and Racism in a Risk-Factor-Based Drug Testing Policy in the Labor and Delivery Department.O&G open · 2024Article
- Childbearing Histories and Midlife Cognition: Accounting for Early Life Factors.The journals of gerontology. Series B, Psychological sciences and social sciences · 2024Article
- Improved outcomes with leadless vs. single-chamber transvenous pacemaker in haemodialysis patients.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Article
- Developing a framework for identifying risk factors and estimating direct economic disease burden attributable to healthcare-associated infections: a case study of a Chinese Tuberculosis hospital.Global health research and policy · 2024Article
- Role of invasive mediastinal nodal staging in survival outcomes of patients with non-small cell lung cancer and without radiologic lymph node metastasis: a retrospective cohort study.EClinicalMedicine · 2024Article
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3 authors.
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Abstract
Propensity score matching is commonly used in observational studies to control for confounding and estimate the causal effects of a treatment or exposure. Frequently, in observational studies data are clustered, which adds to the complexity of using propensity score techniques. In this article, we give an overview of propensity score matching methods for clustered data, and highlight how propensity score matching can be used to account for not just measured confounders, but also unmeasured cluster level confounders. We also consider using machine learning methods such as generalized boosted models to estimate the propensity score and show that accounting for clustering when using these methods can greatly reduce the performance, particularly when there are a large number of clusters and a small number of subjects per cluster. In order to get around this we highlight scenarios where it may be possible to control for measured covariates using propensity score matching, while using fixed effects regression in the outcome model to control for cluster level covariates. Using simulation studies we compare the performance of different propensity score matching methods for clustered data across a number of different settings. Finally, as an illustrative example we apply propensity score matching methods for clustered data to study the causal effect of aspirin on hearing deterioration using data from the conservation of hearing study.
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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.