The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments

Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time‐to‐event in nature. Propensity‐score methods are often applied incorrectly when estimating the effect of treatment...

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Vydáno v:	Statistics in medicine Ročník 33; číslo 7; s. 1242 - 1258
Hlavní autor:	Austin, Peter C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Blackwell Publishing Ltd 30.03.2014 Wiley Subscription Services, Inc BlackWell Publishing Ltd
Témata:	Aged Clinical outcomes Clinical trials confounding Confounding Factors (Epidemiology) Epidemiology event history analysis Female Humans Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use inverse probability of treatment weighting Male marginal effects Medical statistics Medical treatment Middle Aged Myocardial Infarction - drug therapy Observational Studies as Topic - methods observational study Propensity Score propensity score matching Side effects Survival Analysis Treatment Outcome Tutorial in Biostatistics confounding propensity score matching event history analysis inverse probability of treatment weighting survival analysis marginal effects observational study propensity score
ISSN:	0277-6715, 1097-0258, 1097-0258
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Shrnutí:	Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time‐to‐event in nature. Propensity‐score methods are often applied incorrectly when estimating the effect of treatment on time‐to‐event outcomes. This article describes how two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials: (i) marginal survival curves, which describe survival in the population if all subjects were treated or if all subjects were untreated; and (ii) marginal hazard ratios. The use of these propensity score methods allows one to replicate the measures of effect that are commonly reported in randomized controlled trials with time‐to‐event outcomes: both absolute and relative reductions in the probability of an event occurring can be determined. We also provide guidance on variable selection for the propensity score model, highlight methods for assessing the balance of baseline covariates between treated and untreated subjects, and describe the implementation of a sensitivity analysis to assess the effect of unmeasured confounding variables on the estimated treatment effect when outcomes are time‐to‐event in nature. The methods in the paper are illustrated by estimating the effect of discharge statin prescribing on the risk of death in a sample of patients hospitalized with acute myocardial infarction. In this tutorial article, we describe and illustrate all the steps necessary to conduct a comprehensive analysis of the effect of treatment on time‐to‐event outcomes. © 2013 The authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
Bibliografie:	istex:57C274529C6BEC9F6EC00FC5979ABCA62DC8D90A ark:/67375/WNG-FK7VQCB5-D ArticleID:SIM5984 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 E-mail: peter.austin@ices.on.ca
ISSN:	0277-6715 1097-0258 1097-0258
DOI:	10.1002/sim.5984