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The performance of different propensity-score methods for estimating relative risks.
J Clin Epidemiol. 2008 Jun; 61(6):537-45.JC

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.

Authors+Show Affiliations

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

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

18471657

Citation

Austin, Peter C.. "The Performance of Different Propensity-score Methods for Estimating Relative Risks." Journal of Clinical Epidemiology, vol. 61, no. 6, 2008, pp. 537-45.
Austin PC. The performance of different propensity-score methods for estimating relative risks. J Clin Epidemiol. 2008;61(6):537-45.
Austin, P. C. (2008). The performance of different propensity-score methods for estimating relative risks. Journal of Clinical Epidemiology, 61(6), 537-45. https://doi.org/10.1016/j.jclinepi.2007.07.011
Austin PC. The Performance of Different Propensity-score Methods for Estimating Relative Risks. J Clin Epidemiol. 2008;61(6):537-45. PubMed PMID: 18471657.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The performance of different propensity-score methods for estimating relative risks. A1 - Austin,Peter C, Y1 - 2008/02/14/ PY - 2007/03/23/received PY - 2007/07/10/revised PY - 2007/07/15/accepted PY - 2008/5/13/pubmed PY - 2008/8/30/medline PY - 2008/5/13/entrez SP - 537 EP - 45 JF - Journal of clinical epidemiology JO - J Clin Epidemiol VL - 61 IS - 6 N2 - 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. SN - 0895-4356 UR - https://www.unboundmedicine.com/medline/citation/18471657/The_performance_of_different_propensity_score_methods_for_estimating_relative_risks_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0895-4356(07)00278-8 DB - PRIME DP - Unbound Medicine ER -