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The performance of different propensity score methods for estimating marginal hazard ratios.
Stat Med. 2013 Jul 20; 32(16):2837-49.SM

Abstract

Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non-randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., differences in means or proportions). However, in biomedical research, time-to-event outcomes occur frequently. There is a paucity of research into the performance of different propensity score methods for estimating the effect of treatment on time-to-event outcomes. Furthermore, propensity score methods allow for the estimation of marginal or population-average treatment effects. We conducted an extensive series of Monte Carlo simulations to examine the performance of propensity score matching (1:1 greedy nearest-neighbor matching within propensity score calipers), stratification on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score to estimate marginal hazard ratios. We found that both propensity score matching and IPTW using the propensity score allow for the estimation of marginal hazard ratios with minimal bias. Of these two approaches, IPTW using the propensity score resulted in estimates with lower mean squared error when estimating the effect of treatment in the treated. Stratification on the propensity score and covariate adjustment using the propensity score result in biased estimation of both marginal and conditional hazard ratios. Applied researchers are encouraged to use propensity score matching and IPTW using the propensity score when estimating the relative effect of treatment on time-to-event outcomes.

Authors+Show Affiliations

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

Pub Type(s)

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

Language

eng

PubMed ID

23239115

Citation

Austin, Peter C.. "The Performance of Different Propensity Score Methods for Estimating Marginal Hazard Ratios." Statistics in Medicine, vol. 32, no. 16, 2013, pp. 2837-49.
Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med. 2013;32(16):2837-49.
Austin, P. C. (2013). The performance of different propensity score methods for estimating marginal hazard ratios. Statistics in Medicine, 32(16), 2837-49. https://doi.org/10.1002/sim.5705
Austin PC. The Performance of Different Propensity Score Methods for Estimating Marginal Hazard Ratios. Stat Med. 2013 Jul 20;32(16):2837-49. PubMed PMID: 23239115.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The performance of different propensity score methods for estimating marginal hazard ratios. A1 - Austin,Peter C, Y1 - 2012/12/12/ PY - 2011/11/28/received PY - 2012/11/19/accepted PY - 2012/12/15/entrez PY - 2012/12/15/pubmed PY - 2014/2/12/medline SP - 2837 EP - 49 JF - Statistics in medicine JO - Stat Med VL - 32 IS - 16 N2 - Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non-randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., differences in means or proportions). However, in biomedical research, time-to-event outcomes occur frequently. There is a paucity of research into the performance of different propensity score methods for estimating the effect of treatment on time-to-event outcomes. Furthermore, propensity score methods allow for the estimation of marginal or population-average treatment effects. We conducted an extensive series of Monte Carlo simulations to examine the performance of propensity score matching (1:1 greedy nearest-neighbor matching within propensity score calipers), stratification on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score to estimate marginal hazard ratios. We found that both propensity score matching and IPTW using the propensity score allow for the estimation of marginal hazard ratios with minimal bias. Of these two approaches, IPTW using the propensity score resulted in estimates with lower mean squared error when estimating the effect of treatment in the treated. Stratification on the propensity score and covariate adjustment using the propensity score result in biased estimation of both marginal and conditional hazard ratios. Applied researchers are encouraged to use propensity score matching and IPTW using the propensity score when estimating the relative effect of treatment on time-to-event outcomes. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/23239115/The_performance_of_different_propensity_score_methods_for_estimating_marginal_hazard_ratios_ L2 - https://dx.doi.org/10.1002/sim.5705 DB - PRIME DP - Unbound Medicine ER -