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Cited by 493Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review

Shah, Baiju R., Laupacis, Andreas, Hux, Janet E., Austin, Peter C.·2005·Journal of Clinical Epidemiology
DOI 10.1016/j.jclinepi.2004.10.016
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Abstract

Objective To determine whether adjusting for confounder bias in observational studies using propensity scores gives different results than using traditional regression modeling.Methods Medline and Embase were used to identify studies that described at least one association between an exposure and an outcome using both traditional regression and propensity score methods to control for confounding. From 43 studies, 78 exposure-outcome associations were found. Measures of the quality of propensity score implementation were determined. The statistical significance of each association using both analytical methods was compared. The odds or hazard ratios derived using both methods were compared quantitatively.Results Statistical significance differed between regression and propensity score methods for only 8 of the associations (10%), kappa = 0.79 (95% CI = 0.65-0.92). In all cases, the regression method gave a statistically significant association not observed with the propensity score method. The odds or hazard ratio derived using propensity scores was, on average, 6.4% closer to unity than that derived using traditional regression.Conclusions Observational studies had similar results whether using traditional regression or propensity scores to adjust for confounding. Propensity scores gave slightly weaker associations; however, many of the reviewed studies did not implement propensity scores well.

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Crossref · 10.1016/j.jclinepi.2004.10.016

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  • Although in statistical terms the use of propensity scores may produce results that differ little from those obtained through traditional regression adjustment, they encourage clearer thinking about study design and particularly the assignment mechanism.

    “Although in statistical terms the use of propensity scores may produce results that differ little from those obtained through traditional regression adjustment (70), they encourage clearer thinking about study design and particularly the assignment mechanism (66).”

    From Natural Experiments: An Overview of Methods, Approaches, and Contributions to Public Health Intervention Research (2017) · cites this paper as [70]

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Authors
Shah, B. R., Laupacis, A., Hux, J. E., & Austin, P. C.
Affiliations
University of Toronto (CA)
Corresponding author
Shah, Baiju R.
ORCID
Venue
Journal of Clinical Epidemiology
Publisher
Elsevier BV
Published
2005-06
Volume
58
Issue
6
Pages
550-559
ISSN
0895-4356 (ISSN-L)
DOI
10.1016/j.jclinepi.2004.10.016
OpenAlex
W2063548620
Cited by
493
References
53
Citations by year
2026: 6 · 2025: 7 · 2024: 13 · 2023: 10 · 2022: 24 · 2021: 20 · 2020: 19 · 2019: 20 · 2018: 24 · 2017: 23 · 2016: 26 · 2015: 30 · 2014: 33 · 2013: 33 · 2012: 42
FWCI
3.19
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Journal of Clinical Epidemiology (publishedVersion)
  • PubMed (publishedVersion)
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, confounding adjustment, observational studies, odds ratio, hazard ratio, statistical significance, exposure-outcome association
MeSH terms
Data Interpretation, Statistical, Humans, Regression Analysis, Research Design, Bias, Confounding Factors, Epidemiologic, Outcome Assessment, Health Care

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