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Tumour incidence, prevalence and lethality estimation in the absence of cause‐of‐death information
Author(s) -
Härkänen T.,
Arjas E.
Publication year - 2004
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2004.05055.x
Subject(s) - lethality , markov chain monte carlo , statistics , hazard ratio , bayesian probability , markov chain , incidence (geometry) , monte carlo method , estimation , hazard , discretization , convergence (economics) , mathematics , medicine , algorithm , computer science , confidence interval , biology , toxicology , engineering , ecology , geometry , systems engineering , mathematical analysis , economics , economic growth
Summary. A Bayesian intensity model is presented for studying a bioassay problem involving interval‐censored tumour onset times, and without discretization of times of death. Both tumour lethality and base‐line hazard rates are estimated in the absence of cause‐of‐death information. Markov chain Monte Carlo methods are used in the numerical estimation, and sophisticated group updating algorithms are applied to achieve reasonable convergence properties. This method was tried on the rat tumorigenicity data that have previously been analysed by Ahn, Moon and Kodell, and our results seem to be more realistic.
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