Statistical primer: propensity score matching and its alternatives
Open accessAbstract
Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS methods estimate the treatment effect by modelling the relationship between confounders and treatment assignment. Therefore, methods based on the PS are not limited by the number of events, and their use may be warranted when the number of confounders is large, or the number of outcomes is small. The PS is the probability for a subject to receive a treatment conditional on a set of baseline characteristics (confounders). The PS is commonly estimated using logistic regression, and it is used to match patients with similar distribution of confounders so that difference in outcomes gives unbiased estimate of treatment effect. This review summarizes basic concepts of the PS matching and provides guidance in implementing matching and other methods based on the PS, such as stratification, weighting and covariate adjustment.
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Propensity score matching is considered a robust method.
“PSM is considered a robust method (Austin, 2011; Benedetto et al., 2018).”
From Midwifery continuity of care, breastfeeding and neonatal hyperbilirubinemia: A retrospective cohort study (2024) · cites this paper as Benedetto et al., 2018
Propensity score matching is an analytic approach to reduce bias of measured confounding effects.
“PSM is an analytic approach to reduce bias of measured confounding effects from covariates (Austin, 2011; Benedetto et al., 2018).”
From Midwifery continuity of care, breastfeeding and neonatal hyperbilirubinemia: A retrospective cohort study (2024) · cites this paper as Benedetto et al., 2018
Cites
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758 · see citing works
A comparison of 12 algorithms for matching on the propensity score
Austin, P. C. (2014) · Statistics in Medicine · doi:10.1002/sim.6004 · Cited by 1,085 · see citing works
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961 · see citing works
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Mody, R., Manjelievskaia, J., Marchlewicz, E. H., Malik, R. E., Zimmerman, N. M., Irwin, D. E., & Yu, M. (2022) · Clinical Therapeutics · doi:10.1016/j.clinthera.2022.01.017 · Cited by 22 · see citing works
Impact of compulsory admission on treatment and outcome: A propensity score matched analysis
Hofmann, A. B., Schmid, H. M., Hofmann, L. A., Noboa, V., Seifritz, E., Vetter, S., & Egger, S. T. (2022) · European Psychiatry · doi:10.1192/j.eurpsy.2022.4 · Cited by 19 · see citing works
Bottigliengo, D., Lorenzoni, G., Ocagli, H., Martinato, M., Berchialla, P., & Gregori, D. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18136694 · Cited by 13 · see citing works
Shahshahani, M. A., Liu, X., Norman, M., Tilden, E. L., & Ahlberg, M. (2024) · Midwifery · doi:10.1016/j.midw.2024.104079 · Cited by 7 · see citing works
- Authors
- Benedetto, U., Head, S. J., Angelini, G. D., & Blackstone, E. H.
- Affiliations
- University of Bristol (GB)
- Erasmus MC (NL)
- Cleveland Clinic (US)
- Corresponding author
- Benedetto, Umberto
- Venue
- European Journal of Cardio-Thoracic Surgery
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-19
- Volume
- 53
- Issue
- 6
- Pages
- 1112-1117
- ISSN
- 1010-7940 (ISSN-L)
- 1873-734X
- OpenAlex
- W2803832675
- Cited by
- 695
- References
- 3
- Citations by year
- 2026: 80 · 2025: 129 · 2024: 136 · 2023: 107 · 2022: 112 · 2021: 76 · 2020: 34 · 2019: 15 · 2018: 4
- FWCI
- 26.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- European Journal of Cardio-Thoracic Surgery (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research (NF-SI-0611-10022)
- British Heart Foundation (CH/1992027/7163, PG/11/19/28827, PG/13/9/29990)
- Medical Research Council (MR/J015350/1)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- propensity score matching, confounding by indication, observational studies, treatment effect estimation, stratification, inverse probability weighting, covariate adjustment, multivariable regression, logistic regression
- MeSH terms
- Humans, Models, Statistical, Randomized Controlled Trials as Topic, Propensity Score
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