Indications for propensity scores and review of their use in pharmacoepidemiology

Glynn, Robert J.; Schneeweiss, Sebastian; Stürmer, Til

doi:10.1111/j.1742-7843.2006.pto_293.x

Cited by 541Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

Indications for propensity scores and review of their use in pharmacoepidemiology

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Glynn, Robert J., Schneeweiss, Sebastian, Stürmer, Til·2006·Basic & Clinical Pharmacology & Toxicology

DOI 10.1111/j.1742-7843.2006.pto_293.xPMC PMC1790968

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Abstract

Abstract: Use of propensity scores to identify and control for confounding in observational studies that relate medications to outcomes has increased substantially in recent years. However, it remains unclear whether, and if so when, use of propensity scores provides estimates of drug effects that are less biased than those obtained from conventional multivariate models. In the great majority of published studies that have used both approaches, estimated effects from propensity score and regression methods have been similar. Simulation studies further suggest comparable performance of the two approaches in many settings. We discuss five reasons that favour use of propensity scores: the value of focus on indications for drug use; optimal matching strategies from alternative designs; improved control of confounding with scarce outcomes; ability to identify interactions between propensity of treatment and drug effects on outcomes; and correction for unobserved confounders via propensity score calibration. We describe alternative approaches to estimate and implement propensity scores and the limitations of the C‐statistic for evaluation. Use of propensity scores will not correct biases from unmeasured confounders, but can aid in understanding determinants of drug use and lead to improved estimates of drug effects in some settings.

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Authors: Glynn, R. J., Schneeweiss, S., & Stürmer, T.
Affiliations: Brigham and Women's Hospital (US)
Corresponding author: Glynn, Robert J.
ORCID: Glynn, Robert J.
Schneeweiss, Sebastian
Venue: Basic & Clinical Pharmacology & Toxicology
Publisher: Wiley
Published: 2006-03
Volume: 98
Issue: 3
Pages: 253-259
ISSN: 1742-7835 (ISSN-L)
1742-7843
DOI: 10.1111/j.1742-7843.2006.pto_293.x
PMC: PMC1790968
OpenAlex: W2073225472
Cited by: 541
References: 32
Citations by year: 2026: 8 · 2025: 11 · 2024: 15 · 2023: 19 · 2022: 29 · 2021: 29 · 2020: 33 · 2019: 43 · 2018: 36 · 2017: 33 · 2016: 41 · 2015: 37 · 2014: 35 · 2013: 43 · 2012: 35
FWCI: 2.80
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 15 d)
Locations: Basic & Clinical Pharmacology & Toxicology (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute on Aging (AG-18833, R56AG023178, AG-23178, R56AG018833, R01AG018833, R01AG023178)
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, pharmacoepidemiology, confounding adjustment, observational studies, propensity score matching, unmeasured confounding, concordance index, simulation study
MeSH terms: Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Research Design, Models, Statistical, Bias, Confounding Factors, Epidemiologic, Treatment Outcome, Outcome Assessment, Health Care, Pharmacoepidemiology, Drug-Related Side Effects and Adverse Reactions

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Indications for propensity scores and review of their use in pharmacoepidemiology

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