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The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments.
Stat Med. 2014 Mar 30; 33(7):1242-58.SM

Abstract

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.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Toronto, Canada; Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.

Pub Type(s)

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

Language

eng

PubMed ID

24122911

Citation

Austin, Peter C.. "The Use of Propensity Score Methods With Survival or Time-to-event Outcomes: Reporting Measures of Effect Similar to Those Used in Randomized Experiments." Statistics in Medicine, vol. 33, no. 7, 2014, pp. 1242-58.
Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Stat Med. 2014;33(7):1242-58.
Austin, P. C. (2014). The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Statistics in Medicine, 33(7), 1242-58. https://doi.org/10.1002/sim.5984
Austin PC. The Use of Propensity Score Methods With Survival or Time-to-event Outcomes: Reporting Measures of Effect Similar to Those Used in Randomized Experiments. Stat Med. 2014 Mar 30;33(7):1242-58. PubMed PMID: 24122911.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. A1 - Austin,Peter C, Y1 - 2013/09/30/ PY - 2012/10/23/received PY - 2013/08/22/revised PY - 2013/09/03/accepted PY - 2013/10/15/entrez PY - 2013/10/15/pubmed PY - 2014/11/7/medline KW - confounding KW - event history analysis KW - inverse probability of treatment weighting KW - marginal effects KW - observational study KW - propensity score KW - propensity score matching KW - survival analysis SP - 1242 EP - 58 JF - Statistics in medicine JO - Stat Med VL - 33 IS - 7 N2 - 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. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/24122911/The_use_of_propensity_score_methods_with_survival_or_time_to_event_outcomes:_reporting_measures_of_effect_similar_to_those_used_in_randomized_experiments_ L2 - https://dx.doi.org/10.1002/sim.5984 DB - PRIME DP - Unbound Medicine ER -