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J Clin Epidemiol. 2008 Jun;61(6):537-45. doi: 10.1016/j.jclinepi.2007.07.011. Epub 2008 Feb 14.

The performance of different propensity-score methods for estimating relative risks.

Author information

  • Institute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada. peter.austin@ices.on.ca

Abstract

OBJECTIVES:

The propensity score is the probability of treatment conditional on observed variables. Conditioning on the propensity-score results in unbiased estimation of the expected difference in observed responses to two treatments. The performance of propensity-score methods for estimating relative risks has not been studied.

STUDY DESIGN AND SETTING:

Monte Carlo simulations were used to assess the performance of matching, stratification, and covariate adjustment using the propensity score to estimate relative risks.

RESULTS:

Matching on the propensity score and stratification on the quintiles of the propensity score resulted in estimates of relative risk with similar mean squared error (MSE). Propensity-score matching resulted in estimates with less bias, whereas stratification on the propensity score resulted in estimates of with greater precision. Including only variables associated with the outcome or including only the true confounders in the propensity-score model resulted in estimates with lower MSE than did including all variables associated with treatment or all measured variables in the propensity-score model.

CONCLUSIONS:

When estimating relative risks, propensity-score matching resulted in estimates with less bias than did stratification on the quintiles of the propensity score, but stratification on the quintiles of the propensity score resulted in estimates with greater precision.

PMID:
18471657
[PubMed - indexed for MEDLINE]
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