Document Detail


Interval estimation for treatment effects using propensity score matching.
MedLine Citation:
PMID:  16220488     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.
Authors:
Jennifer Hill; Jerome P Reiter
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  25     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2006 Jul 
Date Detail:
Created Date:  2006-06-13     Completed Date:  2006-11-06     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  2230-56     Citation Subset:  IM    
Affiliation:
School of International and Public Affairs, Columbia University, 420 West 118th St., 740 IAB, New York, NY 10027, USA. jh1030@columbia.edu
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MeSH Terms
Descriptor/Qualifier:
Adolescent
African Americans
Cognition
Computer Simulation
Data Interpretation, Statistical*
Female
Humans
Infant
Infant, Low Birth Weight / growth & development
Infant, Newborn
Infant, Premature / growth & development
Male
Socioeconomic Factors
Treatment Outcome*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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