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The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies.
Stat Med. 2010 Sep 10; 29(20):2137-48.SM

Abstract

Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Toronto, ON, Canada. peter.austin@ices.on.ca

Pub Type(s)

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

Language

eng

PubMed ID

20108233

Citation

Austin, Peter C.. "The Performance of Different Propensity-score Methods for Estimating Differences in Proportions (risk Differences or Absolute Risk Reductions) in Observational Studies." Statistics in Medicine, vol. 29, no. 20, 2010, pp. 2137-48.
Austin PC. The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Stat Med. 2010;29(20):2137-48.
Austin, P. C. (2010). The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Statistics in Medicine, 29(20), 2137-48. https://doi.org/10.1002/sim.3854
Austin PC. The Performance of Different Propensity-score Methods for Estimating Differences in Proportions (risk Differences or Absolute Risk Reductions) in Observational Studies. Stat Med. 2010 Sep 10;29(20):2137-48. PubMed PMID: 20108233.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. A1 - Austin,Peter C, PY - 2010/1/29/entrez PY - 2010/1/29/pubmed PY - 2010/12/17/medline SP - 2137 EP - 48 JF - Statistics in medicine JO - Stat Med VL - 29 IS - 20 N2 - Propensity score methods are increasingly being used to estimate the effects of treatments on health outcomes using observational data. There are four methods for using the propensity score to estimate treatment effects: covariate adjustment using the propensity score, stratification on the propensity score, propensity-score matching, and inverse probability of treatment weighting (IPTW) using the propensity score. When outcomes are binary, the effect of treatment on the outcome can be described using odds ratios, relative risks, risk differences, or the number needed to treat. Several clinical commentators suggested that risk differences and numbers needed to treat are more meaningful for clinical decision making than are odds ratios or relative risks. However, there is a paucity of information about the relative performance of the different propensity-score methods for estimating risk differences. We conducted a series of Monte Carlo simulations to examine this issue. We examined bias, variance estimation, coverage of confidence intervals, mean-squared error (MSE), and type I error rates. A doubly robust version of IPTW had superior performance compared with the other propensity-score methods. It resulted in unbiased estimation of risk differences, treatment effects with the lowest standard errors, confidence intervals with the correct coverage rates, and correct type I error rates. Stratification, matching on the propensity score, and covariate adjustment using the propensity score resulted in minor to modest bias in estimating risk differences. Estimators based on IPTW had lower MSE compared with other propensity-score methods. Differences between IPTW and propensity-score matching may reflect that these two methods estimate the average treatment effect and the average treatment effect for the treated, respectively. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/20108233/The_performance_of_different_propensity_score_methods_for_estimating_differences_in_proportions__risk_differences_or_absolute_risk_reductions__in_observational_studies_ L2 - https://dx.doi.org/10.1002/sim.3854 DB - PRIME DP - Unbound Medicine ER -