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Do observational studies using propensity score methods agree with randomized trials? A systematic comparison of studies on acute coronary syndromes
Open accessAbstract
AIMS: Randomized controlled trials (RCTs) are the gold standard for assessing the efficacy of therapeutic interventions because randomization protects from biases inherent in observational studies. Propensity score (PS) methods, proposed as a potential solution to confounding of the treatment-outcome association, are widely used in observational studies of therapeutic interventions for acute coronary syndromes (ACS). We aimed to systematically assess agreement between observational studies using PS methods and RCTs on therapeutic interventions for ACS. METHODS AND RESULTS: We searched for observational studies of interventions for ACS that used PS methods to estimate treatment effects on short- or long-term mortality. Using a standardized algorithm, we matched observational studies to RCTs based on patients' characteristics, interventions, and outcomes ('topics'), and we compared estimates of treatment effect between the two designs. When multiple observational studies or RCTs were identified for the same topic, we performed a meta-analysis and used the summary relative risk for comparisons. We matched 21 observational studies investigating 17 distinct clinical topics to 63 RCTs (median = 3 RCTs per observational study) for short-term (7 topics) and long-term (10 topics) mortality. Estimates from PS analyses differed statistically significantly from randomized evidence in two instances; however, observational studies reported more extreme beneficial treatment effects compared with RCTs in 13 of 17 instances (P = 0.049). Sensitivity analyses limited to large RCTs, and using alternative meta-analysis models yielded similar results. CONCLUSION: For the treatment of ACS, observational studies using PS methods produce treatment effect estimates that are of more extreme magnitude compared with those from RCTs, although the differences are rarely statistically significant.
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“Such methods often yield results similar to those in controlled treatment trials (133, 134).”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [133]
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Agentic Trial Emulation to Learn Health System-specific Drug Effects At ScalePreprint
Kauffman, J., Duan, L., Gelman, S., Klang, E., Sakhuja, A., Bhatt, D. L., Reddy, V. Y. Y., Charney, A. W., Nadkarni, G. N., Qu, Y., et al. (2026) · medRxiv · doi:10.64898/2026.02.19.26346539
Lonjon, G. (2017) · theses.fr (ABES) · doi:10.70675/199cef50z1485z4701z9f6az5371bc8ec0a0
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“Nonetheless, methods exist to correct for this bias and time-dependent confounding bias, such as propensity scores 37 or instrumental variables, G- computation, marginal structural models 38 and the target trial framework.”
Metadata
- Authors
- Dahabreh, I. J., Sheldrick, R. C., Paulus, J. K., Chung, M., Varvarigou, V., Jafri, H., Rassen, J. A., Trikalinos, T. A., & Kitsios, G. D.
- Affiliations
- Tufts Medical Center (US)
- Tufts University (US)
- Harvard University (US)
- Johns Hopkins University (US)
- Brigham and Women's Hospital (US)
- Lahey Hospital and Medical Center (US)
- Corresponding author
- Dahabreh, Issa J.
- ORCID
- Venue
- European Heart Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-06-17
- Volume
- 33
- Issue
- 15
- Pages
- 1893-1901
- ISSN
- 0195-668X (ISSN-L)
- 1522-9645
- PMC
- PMC3409422
- OpenAlex
- W2162409127
- Cited by
- 204
- References
- 40
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 10 · 2023: 10 · 2022: 23 · 2021: 22 · 2020: 18 · 2019: 14 · 2018: 18 · 2017: 15 · 2016: 12 · 2015: 13 · 2014: 24 · 2013: 10 · 2012: 2
- FWCI
- 14.97
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- European Heart Journal (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Center for Research Resources (UL1RR025752)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- propensity score methods, acute coronary syndrome, observational studies, randomized controlled trials, treatment effect estimation, confounding, mortality
- MeSH terms
- Humans, Regression Analysis, Risk Factors, Reproducibility of Results, Randomized Controlled Trials as Topic, Treatment Outcome, Acute Coronary Syndrome, Propensity Score
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