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Cited by 356

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies

Open access
Austin, Peter C.·2010·Statistics in Medicine
DOI 10.1002/sim.3854
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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.

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Metadata

Authors
Austin, P. C.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2010-01-27
Volume
29
Issue
20
Pages
2137-2148
ISSN
0277-6715 (ISSN-L)
DOI
10.1002/sim.3854
OpenAlex
W1975994236
Cited by
356
References
39
FWCI
15.62
Type
journal article
Language
en
Open access
bronze

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