The performance of different propensity score methods for estimating marginal hazard ratios

By Peter C. Austin. Statistics in Medicine, 2007

886 citations recorded by the source corpus.

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. Copyright © 2012 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/360e8606301ecfe41ab375788340719ea4bc16ee

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Citations in the corpus

The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments (2013, methodology): “This approach has been shown to result in biased estimation of marginal hazard ratios [30].”
Which covariates should be controlled in propensity score matching? Evidence from a simulation study (2013, methodology): “To examine the properties of matching estimators, many studies rely on Monte Carlo simulations (e.g. Frölich, 2004; Zhao, 2004; Austin, 2007; Zhao, 2008; Ghosh, 2011).”
An Examination of the Impact of Accelerating Community College Students' Progression through Developmental Education (2014, methodology): “6 many experts in propensity score matching (e.g., Austin, 2007; imai, king, & Stuart, 2008) do not recommend using t-tests to assess balance because t-tests, or hypothesis tests, are estimated using sample size.”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, background): “Prior Monte Carlo studies have compared the relative performance of the different propensity score methods for estimating risk differences, relative risks, and marginal and conditional odds ratios (Austin, 2007b, 2008c, 2010; Austin, Grootendorst, Normand, & Anderson, 2007).”