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Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study.
Pharm Stat. 2012 May-Jun; 11(3):222-9.PS

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.

Authors+Show Affiliations

Clinical Epidemiology and Biostatistics, Inserm U717, Paris France; Université Paris Diderot, Paris, France. etienne.gayat@9online.fr.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

22411785

Citation

Gayat, Etienne, et al. "Propensity Score Applied to Survival Data Analysis Through Proportional Hazards Models: a Monte Carlo Study." Pharmaceutical Statistics, vol. 11, no. 3, 2012, pp. 222-9.
Gayat E, Resche-Rigon M, Mary JY, et al. Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study. Pharm Stat. 2012;11(3):222-9.
Gayat, E., Resche-Rigon, M., Mary, J. Y., & Porcher, R. (2012). Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study. Pharmaceutical Statistics, 11(3), 222-9. https://doi.org/10.1002/pst.537
Gayat E, et al. Propensity Score Applied to Survival Data Analysis Through Proportional Hazards Models: a Monte Carlo Study. Pharm Stat. 2012 May-Jun;11(3):222-9. PubMed PMID: 22411785.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study. AU - Gayat,Etienne, AU - Resche-Rigon,Matthieu, AU - Mary,Jean-Yves, AU - Porcher,Raphaël, Y1 - 2012/03/12/ PY - 2011/04/11/received PY - 2011/08/30/revised PY - 2011/11/28/accepted PY - 2012/3/14/entrez PY - 2012/3/14/pubmed PY - 2015/7/4/medline KW - bias KW - propensity score KW - simulation KW - survival KW - treatment effect SP - 222 EP - 9 JF - Pharmaceutical statistics JO - Pharm Stat VL - 11 IS - 3 N2 - 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. SN - 1539-1612 UR - https://www.unboundmedicine.com/medline/citation/22411785/Propensity_score_applied_to_survival_data_analysis_through_proportional_hazards_models:_a_Monte_Carlo_study_ L2 - https://doi.org/10.1002/pst.537 DB - PRIME DP - Unbound Medicine ER -