This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Borgan, O. Search for Related Content PubMed PubMed Citation Articles by Borgan, O. Pubmed/NCBI databases Compound via MeSH Substance via MeSH Hazardous Substances DB URANIUM COMPOUNDS URANIUM, ELEMENTAL Social Bookmarking                         What's this?

Estimation of covariate-dependent Markov transition probabilities from nested case-control data

Department of Mathematics, University of Oslo, Norway, borgan{at}math.uio.no

Multi-state models are used to describe situations where individuals may move among a finite number of states defined by specific conditions of health, including death. The transition intensities of the models are described by proportional hazards models, and it is reviewed how estimation of the regression parameters and the baseline transition intensities may be performed when only nested case-control data are available for all or some of the transitions. The regression parameter estimates and the estimates of baseline transition intensities are combined to give estimates of the integrated transition intensities for specified covariate histories, and from these estimates covariate-dependent Markov transition probabilities are derived.

Statistical Methods in Medical Research, Vol. 11, No. 2, 183-202 (2002)
DOI: 10.1191/0962280202sm280ra


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				P. K. Andersen and N. Keiding Multi-state models for event history analysis Statistical Methods in Medical Research, April 1, 2002; 11(2): 91 - 115. [Abstract] [PDF]