An Introduction to Propensity Score Analysis

There has been a growing interest in using propensity scores in the analysis of observational studies. The propensity score is a balancing mechanism that works to create groups of subjects which have a similar distribution on background covariates. Matching, stratification, inverse propensity treatm...

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Main Author: Cousineau, Valerie Elaine
Other Authors: Bell, Melanie L.
Language:en_US
Published: The University of Arizona. 2016
Subjects:
Online Access:http://hdl.handle.net/10150/613406
http://arizona.openrepository.com/arizona/handle/10150/613406
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spelling ndltd-arizona.edu-oai-arizona.openrepository.com-10150-6134062016-06-18T03:00:59Z An Introduction to Propensity Score Analysis Cousineau, Valerie Elaine Bell, Melanie L. Bedrick, Edward J. Billheimer, David D. Biostatistics Propensity Score There has been a growing interest in using propensity scores in the analysis of observational studies. The propensity score is a balancing mechanism that works to create groups of subjects which have a similar distribution on background covariates. Matching, stratification, inverse propensity treatment weighting and regression adjustment are all strategies that can be used with the propensity score to create balance between groups of subjects. The aim of this paper is to introduce propensity scores and the different techniques which make use of them. We use data obtained from the Women's Health Initiative to demonstrate each of the different methods for propensity score analysis. In the example analysis we examined the association between dog ownership and CVD. The results of our analysis were quite consistent, and demonstrate the propensity score analysis can be used effectively to balance treated and untreated groups within an observational study. 2016 text Electronic Thesis http://hdl.handle.net/10150/613406 http://arizona.openrepository.com/arizona/handle/10150/613406 en_US Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. The University of Arizona.
collection NDLTD
language en_US
sources NDLTD
topic Biostatistics
Propensity Score
spellingShingle Biostatistics
Propensity Score
Cousineau, Valerie Elaine
An Introduction to Propensity Score Analysis
description There has been a growing interest in using propensity scores in the analysis of observational studies. The propensity score is a balancing mechanism that works to create groups of subjects which have a similar distribution on background covariates. Matching, stratification, inverse propensity treatment weighting and regression adjustment are all strategies that can be used with the propensity score to create balance between groups of subjects. The aim of this paper is to introduce propensity scores and the different techniques which make use of them. We use data obtained from the Women's Health Initiative to demonstrate each of the different methods for propensity score analysis. In the example analysis we examined the association between dog ownership and CVD. The results of our analysis were quite consistent, and demonstrate the propensity score analysis can be used effectively to balance treated and untreated groups within an observational study.
author2 Bell, Melanie L.
author_facet Bell, Melanie L.
Cousineau, Valerie Elaine
author Cousineau, Valerie Elaine
author_sort Cousineau, Valerie Elaine
title An Introduction to Propensity Score Analysis
title_short An Introduction to Propensity Score Analysis
title_full An Introduction to Propensity Score Analysis
title_fullStr An Introduction to Propensity Score Analysis
title_full_unstemmed An Introduction to Propensity Score Analysis
title_sort introduction to propensity score analysis
publisher The University of Arizona.
publishDate 2016
url http://hdl.handle.net/10150/613406
http://arizona.openrepository.com/arizona/handle/10150/613406
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