title: Non-parametric Bayesian approach to hazard regression: a case study with a large number of missing covariate values.
abstract: A 'packaged' non-parametric multiplicative hazard regression model is proposed, and applied to a study of the effects of some genetic and viral factors in the development of spontaneous leukaemia in mice. Hierarchical modelling and data augmentation are used to deal with the large number of missing covariate values. A Bayesian procedure is adopted, and the Metropolis-Hastings algorithm is used in the numerical computation of the posterior distribution.
authors.affiliation: Department of Mathematical Sciences, University of Oulu, Finland.
authors.forename: E
authors.initials: E
authors.lastname: Arjas
authors.forename: L
authors.initials: L
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D016010:Binomial Distribution; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007938:Leukemia / Q000235:genetics / Q000821:virology; D008390:Markov Chains; D051379:Mice; D015233:Models, Statistical; D009010:Monte Carlo Method; D010641:Phenotype; D016016:Proportional Hazards Models; D012306:Risk; D018709:Statistics, Nonparametric; D016019:Survival Analysis