A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts
dc.contributor.author | Kaciroti, Niko A. | en_US |
dc.contributor.author | Schork, M. Anthony | en_US |
dc.contributor.author | Raghunathan, Trivellore E. | en_US |
dc.contributor.author | Julius, Stevo | en_US |
dc.date.accessioned | 2009-02-03T16:17:08Z | |
dc.date.available | 2010-04-14T17:40:06Z | en_US |
dc.date.issued | 2009-02-15 | en_US |
dc.identifier.citation | Kaciroti, Niko A.; Schork, M. Anthony; Raghunathan, Trivellore; Julius, Stevo (2009). "A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts." Statistics in Medicine 28(4): 572-585. <http://hdl.handle.net/2027.42/61532> | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/61532 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=19072769&dopt=citation | en_US |
dc.description.abstract | Intention-to-treat (ITT) analysis is commonly used in randomized clinical trials. However, the use of ITT analysis presents a challenge: how to deal with subjects who drop out. Here we focus on randomized trials where the primary outcome is a binary endpoint. Several approaches are available for including the dropout subject in the ITT analysis, mainly chosen prior to unblinding the study. These approaches reduce the potential bias due to breaking the randomization code. However, the validity of the results will highly depend on untestable assumptions about the dropout mechanism. Thus, it is important to evaluate the sensitivity of the results across different missing-data mechanisms. We propose here a Bayesian pattern-mixture model for ITT analysis of binary outcomes with dropouts that applies over different types of missing-data mechanisms. We introduce a new parameterization to identify the model, which is then used for sensitivity analysis. The parameterization is defined as the odds ratio of having an endpoint between the subjects who dropped out and those who completed the study. Such parameterization is intuitive and easy to use in sensitivity analysis; it also incorporates most of the available methods as special cases. The model is applied to TRial Of Preventing HYpertension. Copyright © 2008 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 183092 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Mathematics and Statistics | en_US |
dc.title | A Bayesian sensitivity model for intention-to-treat analysis on binary outcomes with dropouts | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Center for Human Growth and Development, University of Michigan, Ann Arbor, MI 48109, U.S.A. ; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. ; Department of Bioinformatics, University of Michigan, Ann Arbor, MI 48109, U.S.A. ; Center for Human Growth and Development, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.identifier.pmid | 19072769 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/61532/1/3494_ftp.pdf | |
dc.identifier.doi | http://dx.doi.org/10.1002/sim.3494 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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