Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints
dc.contributor.author | Yarnold, Paul R | |
dc.contributor.author | Linden, Ariel | |
dc.date.accessioned | 2017-06-28T20:16:42Z | |
dc.date.available | 2017-06-28T20:16:42Z | |
dc.date.issued | 2017-06-08 | |
dc.identifier.citation | Yarnold PR, Linden A. Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints. Optimal Data Analysis, Vol. 6 (June 8, 2017), 43-46 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/137654 | |
dc.description.abstract | The 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.iso | en_US | en_US |
dc.publisher | Optimal Data Analysis, LLC | en_US |
dc.title | Computing Propensity Score Weights for CTA Models Involving Perfectly Predicted Endpoints | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Internal Medicine | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/137654/1/V6A7.pdf | |
dc.identifier.source | Optimal Data Analysis | en_US |
dc.owningcollname | Internal Medicine, Department of |
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