ArticleHealth services & outcomes research methodology2016
Propensity score weighting for a continuous exposure with multilevel data.
Article in Health services & outcomes research methodology, 2016. 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.
- Inverse Probability Weights for Quasicontinuous Ordinal Exposures With a Binary Outcome: Method Comparison and Case Study.American journal of epidemiology · 2023Trial
- Effects of Exposure to the Communities That Care Prevention System on Youth Problem Behaviors in a Community-Randomized Trial: Employing an Inverse Probability Weighting Approach.Evaluation & the health professions · 2018Trial
- Perceived Racism, Brain Development, and Internalizing and Externalizing Symptoms: Findings From the ABCD Study.Journal of the American Academy of Child and Adolescent Psychiatry · 2026Article
- Multi-hospital electronic decision support for drug-associated acute kidney injury (MEnD-AKI): Study protocol for a randomized clinical trial.Contemporary clinical trials · 2025Article
- Inverse probability weighting for causal inference in hierarchical data.BMC medical research methodology · 2025Article
- A Two-Stage Method for Extending Inferences From a Collection of Trials.Statistics in medicine · 2025Article
- Specific contribution of cognitive and motor impairments with functional capacity and dependence in Huntington's disease.Journal of neurology · 2025Article
- Bayesian doubly robust estimation of causal effects for clustered observational data.Journal of applied statistics · 2025Article
- Leveraging public health infrastructures to support sexual health for women living in the Southern United States.Frontiers in medicine · 2025Article
- Childhood PMProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes.NPJ digital medicine · 2024Article
- Use of Healthcare Claims Data to Generate Real-World Evidence on Patients With Drug-Resistant Epilepsy: Practical Considerations for Research.Journal of health economics and outcomes research · 2024Article
- Validation of the Enhanced Recovery after Surgery (ERAS) society recommendations for liver surgery: a prospective, observational study.Hepatobiliary surgery and nutrition · 2023Article
- The Role of Pre-Treatment Traumatic Stress Symptoms in Adolescent Substance Use Treatment Outcomes.Substance use & misuse · 2023Article
- Random effects modellingFrontiers in pharmacology · 2023Article
- Flexible propensity score estimation strategies for clustered data in observational studies.Statistics in medicine · 2022Article
- Propensity score methods for observational studies with clustered data: A review.Statistics in medicine · 2022Review
- An introduction to inverse probability of treatment weighting in observational research.Clinical kidney journal · 2022Article
- Article
- Propensity score stratification methods for continuous treatments.Statistics in medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. These methods were developed in the context of data with no hierarchical structure or clustering. Yet in many applications the data have a clustered structure that is of substantive importance, such as when individuals are nested within healthcare providers or within schools. Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures. In this paper, we focus on propensity score weighting for a continuous, rather than binary, exposure in a multilevel setting. Using simulations, we compare several specifications of the propensity score: a random effects model, a fixed effects model, and a single-level model. Additionally, our simulations compare the performance of marginal versus cluster-mean stabilized propensity score weights. In our results, regression specifications that accounted for the multilevel structure reduced bias, particularly when cluster-level confounders were omitted. Furthermore, cluster mean weights outperformed marginal weights.
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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.