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Cited by 138Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyStatistical Methods in Clinical Trials

Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders

Arbogast, P. G., Ray, W. A.·2011·American Journal of Epidemiology
DOI 10.1093/aje/kwr143
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Abstract

Propensity scores are widely used in cohort studies to improve performance of regression models when considering large numbers of covariates. Another type of summary score, the disease risk score (DRS), which estimates disease probability conditional on nonexposure, has also been suggested. However, little is known about how it compares with propensity scores. Monte Carlo simulations were conducted comparing regression models using the DRS and the propensity score with models that directly adjust for all of the individual covariates. The DRS was calculated in 2 ways: from the unexposed population and from the full cohort. Compared with traditional multivariable outcome regression models, all 3 summary scores had comparable performance for moderate correlation between exposure and covariates and, for strong correlation, the full-cohort DRS and propensity score had comparable performance. When traditional methods had model misspecification, propensity scores and the full-cohort DRS had superior performance. All 4 models were affected by the number of events per covariate, with propensity scores and traditional multivariable outcome regression least affected. These data suggest that, for cohort studies for which covariates are not highly correlated with exposure, the DRS, particularly that calculated from the full cohort, is a useful tool.

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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 [33]

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Related

Authors
Arbogast, P. G., & Ray, W. A.
Affiliations
Vanderbilt University (US)
ORCID
Ray, W. A.
Venue
American Journal of Epidemiology
Publisher
Oxford University Press (OUP)
Published
2011-07-12
Volume
174
Issue
5
Pages
613-620
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
DOI
10.1093/aje/kwr143
OpenAlex
W2142541082
Cited by
138
References
27
Citations by year
2026: 2 · 2025: 12 · 2024: 6 · 2023: 5 · 2022: 12 · 2021: 9 · 2020: 12 · 2019: 11 · 2018: 16 · 2017: 7 · 2016: 14 · 2015: 11 · 2014: 7 · 2013: 7 · 2012: 7
FWCI
5.87
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
disease risk score, propensity score, model misspecification, summary scores, cohort studies
MeSH terms
Epidemiologic Methods, Humans, Probability, Prognosis, Cohort Studies, Bias, Confounding Factors, Epidemiologic, Multivariate Analysis, Logistic Models, Propensity Score

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