A practical guide to propensity score analysis for applied clinical research.
Observational studies are often the only viable options in many clinical settings, especially when it is unethical or infeasible to randomly assign participants to different treatment régimes. In such case propensity score (PS) analysis can be applied to accounting for possible selection bias and th...
| Published in: | Behaviour Research & Therapy Vol. 98; pp. 76 - 91 |
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| Main Authors: | , |
| Format: | Article |
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Elsevier B.V.
Nov2017
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=125194799&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 125194799 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00057967 BHT jtl: Behaviour Research & Therapy issn: 00057967 maglogo: N pubinfo: dt: Nov2017 vid: 98 pid: 2410 pub: Elsevier B.V. artinfo: ui: 125194799 10.1016/j.brat.2017.01.005 ppf: 76 ppct: 15 formats: tig: atl: A practical guide to propensity score analysis for applied clinical research. aug: au: Lee, Jaehoon Little, Todd D. affil: Department of Educational Psychology and Leadership, College of Education, Texas Tech University, United States su: Behavior therapy Propensity score matching Educational psychology Selection bias (Statistics) Classification algorithms sug: subj: Behavior therapy Educational Support Services Propensity score matching Educational psychology Selection bias (Statistics) Classification algorithms keyword: Matching Propensity score R Subclassification Weighting Matching Propensity score R Subclassification Weighting ab: Observational studies are often the only viable options in many clinical settings, especially when it is unethical or infeasible to randomly assign participants to different treatment régimes. In such case propensity score (PS) analysis can be applied to accounting for possible selection bias and thereby addressing questions of causal inference. Many PS methods exist, yet few guidelines are available to aid applied researchers in their conduct and evaluation of a PS analysis. In this article we give an overview of available techniques for PS estimation and application, balance diagnostic, treatment effect estimation, and sensitivity assessment, as well as recent advances. We also offer a tutorial that can be used to emulate the steps of PS analysis. Our goal is to provide information that will bring PS analysis within the reach of applied clinical researchers and practitioners. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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