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Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study

Authors

  • Etienne Gayat,

    Corresponding author
    1. Université Paris Diderot, Paris, France
    • Clinical Epidemiology and Biostatistics, Inserm U717, Paris France
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  • Matthieu Resche-Rigon,

    1. Clinical Epidemiology and Biostatistics, Inserm U717, Paris France
    2. Université Paris Diderot, Paris, France
    3. Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, Paris, France
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  • Jean-Yves Mary,

    1. Clinical Epidemiology and Biostatistics, Inserm U717, Paris France
    2. Université Paris Diderot, Paris, France
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  • Raphaël Porcher

    1. Clinical Epidemiology and Biostatistics, Inserm U717, Paris France
    2. Université Paris Diderot, Paris, France
    3. Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, Paris, France
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Etienne Gayat, Clinical Epidemiology and Biostatistics, Saint-Louis University Hospital, Inserm U717 1 avenue Claude Vellefaux, 75010 Paris, France.

E-mail: etienne.gayat@9online.fr

Abstract

Propensity score methods are increasingly used in medical literature to estimate treatment effect using data from observational studies. Despite many papers on propensity score analysis, few have focused on the analysis of survival data. Even within the framework of the popular proportional hazard model, the choice among marginal, stratified or adjusted models remains unclear. A Monte Carlo simulation study was used to compare the performance of several survival models to estimate both marginal and conditional treatment effects. The impact of accounting or not for pairing when analysing propensity-score-matched survival data was assessed. In addition, the influence of unmeasured confounders was investigated. After matching on the propensity score, both marginal and conditional treatment effects could be reliably estimated. Ignoring the paired structure of the data led to an increased test size due to an overestimated variance of the treatment effect. Among the various survival models considered, stratified models systematically showed poorer performance. Omitting a covariate in the propensity score model led to a biased estimation of treatment effect, but replacement of the unmeasured confounder by a correlated one allowed a marked decrease in this bias. Our study showed that propensity scores applied to survival data can lead to unbiased estimation of both marginal and conditional treatment effect, when marginal and adjusted Cox models are used. In all cases, it is necessary to account for pairing when analysing propensity-score-matched data, using a robust estimator of the variance. Copyright © 2012 John Wiley & Sons, Ltd.

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