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Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners
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This report aims to provide methodological guidance to help practitioners select the most appropriate weighting method based on propensity scores for their analysis out of many available options (eg, inverse probability treatment weights, standardised mortality ratio weights, fine stratification weights, overlap weights, and matching weights), and outlines recommendations for transparent reporting of studies using weighting based on the propensity scores.
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“Within each cohort, we used propensity score fine stratification weighting to account for confounding.33”
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Metadata
- Authors
- Desai, R. J., & Franklin, J. M.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Desai, Rishi J
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2019-10-23
- Volume
- 367
- Pages
- l5657
- ISSN
- 1756-1833 (ISSN-L)
- 0959-8138
- OpenAlex
- W2982188023
- Cited by
- 657
- References
- 31
- Citations by year
- 2026: 76 · 2025: 142 · 2024: 117 · 2023: 108 · 2022: 108 · 2021: 78 · 2020: 26
- FWCI
- 41.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.bmj.com/company/legal-information/terms-conditions/legal-information/tdm-licencepolicy (tdm)
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Brigham and Women's Hospital
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- propensity score weighting, confounding adjustment, observational studies, reporting transparency, causal inference
- MeSH terms
- Data Interpretation, Statistical, Humans, Research Design, Confounding Factors, Epidemiologic, Survival Rate, Logistic Models, Treatment Outcome, Practice Guidelines as Topic, Propensity Score, Observational Studies as Topic
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