A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality

Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smo...

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Veröffentlicht in:Multivariate behavioral research Jg. 46; H. 1; S. 119 - 151
1. Verfasser: Austin, Peter C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis Group 01.01.2011
Psychology Press
Taylor & Francis Ltd
Taylor & Francis
Schlagworte:
Canada
Case Studies
Comparative Analysis
Computation
Counseling
Counseling Effectiveness
Death
Drug Therapy
Estimating techniques
Foreign Countries
Health Behavior
Heart attacks
Heart Disorders
Hospitalization
Hospitals
Mortality
Outcomes of Treatment
Probability
Program Effectiveness
Research Methodology
Risk
Scores
Smoking
Smoking cessation
Canada
ISSN:0027-3171, 1532-7906, 1532-7906
Online-Zugang:Volltext
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Zusammenfassung:Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being hospitalized with a diagnosis of acute myocardial infarction. The exposure of interest was receipt of smoking cessation counseling prior to hospital discharge and the outcome was mortality with 3 years of hospital discharge. We illustrate the following concepts: first, how to specify the propensity score model; second, how to match treated and untreated participants on the propensity score; third, how to compare the similarity of baseline characteristics between treated and untreated participants after stratifying on the propensity score, in a sample matched on the propensity score, or in a sample weighted by the inverse probability of treatment; fourth, how to estimate the effect of treatment on outcomes when using propensity score matching, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, or covariate adjustment using the propensity score. Finally, we compare the results of the propensity score analyses with those obtained using conventional regression adjustment.
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ISSN:0027-3171
1532-7906
1532-7906
DOI:10.1080/00273171.2011.540480