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Cited by 859Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders

Cepeda, M. S.·2003·American Journal of Epidemiology
DOI 10.1093/aje/kwg115
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Abstract

The aim of this study was to use Monte Carlo simulations to compare logistic regression with propensity scores in terms of bias, precision, empirical coverage probability, empirical power, and robustness when the number of events is low relative to the number of confounders. The authors simulated a cohort study and performed 252,480 trials. In the logistic regression, the bias decreased as the number of events per confounder increased. In the propensity score, the bias decreased as the strength of the association of the exposure with the outcome increased. Propensity scores produced estimates that were less biased, more robust, and more precise than the logistic regression estimates when there were seven or fewer events per confounder. The logistic regression empirical coverage probability increased as the number of events per confounder increased. The propensity score empirical coverage probability decreased after eight or more events per confounder. Overall, the propensity score exhibited more empirical power than logistic regression. Propensity scores are a good alternative to control for imbalances when there are seven or fewer events per confounder; however, empirical power could range from 35% to 60%. Logistic regression is the technique of choice when there are at least eight events per confounder.

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Crossref · 10.1093/aje/kwg115

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  • Risk stratification can control observed confounders more efficiently than regression and may outperform propensity scores when covariates are weakly correlated with exposure.

    “These methods constitute a broader strategy of risk stratification, allowing for more efficient control of observed confounders than regression-based approaches [31,32], and are a better alternative to propensity score estimation when covariates are not highly correlated with the exposure [33].”

    From Social Media Use and Depressive Symptoms Among United States Adolescents (2021) · cites this paper as [31]

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Authors
Cepeda, M. S.
Affiliations
Pontificia Universidad Javeriana (CO)
Corresponding author
Cepeda, M. S.
Venue
American Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
2003-08-01
Volume
158
Issue
3
Pages
280-287
ISSN
0002-9262 (ISSN-L)
DOI
10.1093/aje/kwg115
OpenAlex
W2140074098
Cited by
859
References
30
Citations by year
2026: 15 · 2025: 27 · 2024: 58 · 2023: 42 · 2022: 58 · 2021: 68 · 2020: 68 · 2019: 51 · 2018: 41 · 2017: 50 · 2016: 50 · 2015: 43 · 2014: 37 · 2013: 37 · 2012: 34
FWCI
12.73
Type
journal article
Language
en
Open access
closed
Locations
  • American Journal of 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
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
propensity score matching, logistic regression, small sample size, confounding, bias, statistical power, robustness, cohort studies
MeSH terms
Humans, Research Design, Reproducibility of Results, Models, Statistical, Bias, Confounding Factors, Epidemiologic, Logistic Models, Epidemiologic Studies

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