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A tutorial on propensity score estimation for multiple treatments using generalized boosted models
dc.contributor.authorMcCaffrey, Daniel F.en_US
dc.contributor.authorGriffin, Beth Annen_US
dc.contributor.authorAlmirall, Danielen_US
dc.contributor.authorSlaughter, Mary Ellenen_US
dc.contributor.authorRamchand, Rajeeven_US
dc.contributor.authorBurgette, Lane F.en_US
dc.date.accessioned2013-08-02T20:51:33Z
dc.date.available2014-10-06T19:17:44Zen_US
dc.date.issued2013-08-30en_US
dc.identifier.citationMcCaffrey, Daniel F.; Griffin, Beth Ann; Almirall, Daniel; Slaughter, Mary Ellen; Ramchand, Rajeev; Burgette, Lane F. (2013). "A tutorial on propensity score estimation for multiple treatments using generalized boosted models." Statistics in Medicine 32(19): 3388-3414. <http://hdl.handle.net/2027.42/99037>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/99037
dc.publisherWiley Periodicals, Inc.en_US
dc.publisherSpringer‐Verlag Incen_US
dc.subject.otherCausal Effectsen_US
dc.subject.otherTwangen_US
dc.subject.otherInverse Probability of Treatment Weightingen_US
dc.subject.otherGBMen_US
dc.subject.otherCausal Modelingen_US
dc.titleA tutorial on propensity score estimation for multiple treatments using generalized boosted modelsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.identifier.pmid23508673en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/99037/1/sim_5753_Supplemental_Appendix_R2_12-26-2012.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/99037/2/sim5753.pdf
dc.identifier.doi10.1002/sim.5753en_US
dc.identifier.sourceStatistics in Medicineen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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