Fitting Semiparametric Additive Hazards Models using Standard Statistical Software

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dc.contributor.author Schaubel, Douglas E. en_US
dc.contributor.author Wei, Guanghui en_US
dc.date.accessioned 2007-12-04T18:30:17Z
dc.date.available 2008-11-05T15:05:43Z en_US
dc.date.issued 2007-10 en_US
dc.identifier.citation Schaubel, Douglas E.; Wei, Guanghui (2007). "Fitting Semiparametric Additive Hazards Models using Standard Statistical Software." Biometrical Journal 49(5): 719-730. <http://hdl.handle.net/2027.42/57359> en_US
dc.identifier.issn 0323-3847 en_US
dc.identifier.issn 1521-4036 en_US
dc.identifier.uri http://hdl.handle.net/2027.42/57359
dc.identifier.uri http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=17638295&dopt=citation
dc.description.abstract The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is quite possible that the covariates add to, rather than multiply the baseline hazard, making an additive hazards model a more suitable choice. Typically, proportionality is assumed, with the potential for additive covariate effects not evaluated or even seriously considered. Contributing to this phenomenon is the fact that many popular software packages (e.g., SAS, S-PLUS/R) have standard procedures to fit the Cox model (e.g., proc phreg, coxph), but as of yet no analogous procedures to fit its additive analog, the Lin and Ying (1994) semiparametric additive hazards model. In this article, we establish the connections between the Lin and Ying (1994) model and both Cox and least squares regression. We demonstrate how SAS's phreg and reg procedures may be used to fit the additive hazards model, after some straightforward data manipulations. We then apply the additive hazards model to examine the relationship between Model for End-stage Liver Disease (MELD) score and mortality among patients wait-listed for liver transplantation. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) en_US
dc.format.extent 123560 bytes
dc.format.extent 3118 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.publisher WILEY-VCH Verlag en_US
dc.subject.other Life and Medical Sciences en_US
dc.subject.other Epidemiology, Biostatistics and Public Health en_US
dc.title Fitting Semiparametric Additive Hazards Models using Standard Statistical Software en_US
dc.type Article en_US
dc.rights.robots IndexNoFollow en_US
dc.subject.hlbsecondlevel Biological Chemistry en_US
dc.subject.hlbsecondlevel Physics en_US
dc.subject.hlbtoplevel Science en_US
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Department of Biostatistics, University of Michigan, M4039 SPH II, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA ; Phone: +01 734 615 9825, Fax: +01 734 763 2215 en_US
dc.contributor.affiliationum Department of Biostatistics, University of Michigan, M4039 SPH II, 1420 Washington Heights, Ann Arbor, MI, 48109-2029, USA en_US
dc.identifier.pmid 17638295
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/57359/1/719_ftp.pdf en_US
dc.identifier.doi http://dx.doi.org/10.1002/bimj.200610349 en_US
dc.identifier.source Biometrical Journal en_US
dc.owningcollname Interdisciplinary and Peer-Reviewed
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