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Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints
dc.contributor.authorYarnold, Paul R
dc.contributor.authorLinden, Ariel
dc.date.accessioned2017-06-28T20:16:42Z
dc.date.available2017-06-28T20:16:42Z
dc.date.issued2017-06-08
dc.identifier.citationYarnold PR, Linden A. Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints. Optimal Data Analysis, Vol. 6 (June 8, 2017), 43-46en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/137654
dc.description.abstractThe use of CTA to construct propensity score weights is complicated by division by zero in models having any perfectly predicted endpoints: omitting undefined propensity scores yields a degenerate solution. This note presents an algorithmic remedy to this situation.en_US
dc.language.isoen_USen_US
dc.publisherOptimal Data Analysis, LLCen_US
dc.titleComputing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpointsen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelInternal Medicine
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/137654/1/V6A7.pdf
dc.identifier.sourceOptimal Data Analysisen_US
dc.owningcollnameInternal Medicine, Department of


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