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Hierarchical modelling of multivariate survival data Gustafson, Paul Antony

Abstract

Hierarchical models based on conditional independence are investigated as a means of modelling multivariate survival times. The model structure follows Clayton (1978), Hougaard (1986b), and Oakes (1986, 1989). Both approximate Bayesian and maximum likelihood estimation in these models is investigated via simulation. Predicting a component of a response vector on the basis of other components of the response and other vectors is also studied, using Bayes and empirical Bayes methods. Application to real data is detailed, using the techniques of estimation and prediction discussed.

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