Show simple item record

dc.contributor.authorIsaman, Deanna J. M.en_US
dc.contributor.authorHerman, William H.en_US
dc.contributor.authorBrown, Morton B.en_US
dc.date.accessioned2007-05-01T19:29:02Z
dc.date.available2007-05-01T19:29:02Z
dc.date.issued2006-03-30en_US
dc.identifier.citationIsaman, Deanna J. M.; Herman, William H.; Brown, Morton B. (2006). "A discrete-state discrete-time model using indirect observation." Statistics in Medicine 25(6): 1035-1049. <http://hdl.handle.net/2027.42/50629>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/50629
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=16416413&dopt=citationen_US
dc.description.abstractThis research was motivated by a desire to model the progression of a chronic disease through various disease stages when data are not available to directly estimate all the transition parameters in the model. This is a common occurrence when time and expense make it infeasible to follow a single cohort to estimate all the transition parameters. One difficulty of developing a model of chronic disease progression from such data is that the available studies often do not include the transitions of interest. For example, in our model of diabetic nephropathy, many clinical studies did not differentiate between patients without nephropathy and those who had microalbuminuria (a pre-clinical stage of nephropathy). Another difficulty was a lack of data to directly estimate parameters of interest. We consider models which can accommodate such difficulties. In this paper we consider the problem of estimating parameters of a discrete-time Markov process when longitudinal data describing the entire process are not available. First, we present a likelihood approach to estimate parameters of a discrete-time Markov model. Next, we use simulation to investigate the finite-sample behaviour of our approach. Finally, we present two examples: a model of diabetic nephropathy and a model of cardiovascular disease in diabetes. Copyright © 2006 John Wiley & Sons, Ltd.en_US
dc.format.extent147353 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherJohn Wiley & Sons, Ltd.en_US
dc.subject.otherMathematics and Statisticsen_US
dc.titleA discrete-state discrete-time model using indirect observationen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A. ; School of Nursing, 400 North Ingalls, Room 4245, University of Michigan, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationumDepartment of Internal Medicine and Epidemiology, University of Michigan, Ann Arbor, MI 48109, U.S.A.en_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, U.S.A.en_US
dc.identifier.pmid16416413en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/50629/1/2241_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/sim.2241en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record