Flexible Bayesian modelling for survival data.
The analysis of failure time data often involves two strong assumptions. The proportional hazards assumption postulates that hazard rates corresponding to different levels of explanatory variables are proportional. The additive effects assumption specifies that the effect associated with a particular explanatory variable does not depend on the levels of other explanatory variables. A hierarchical Bayes model is presented, under which both assumptions are relaxed. In particular, time-dependent covariate effects are explicitly modelled, and the additivity of effects is relaxed through the use of a modified neural network structure. The hierarchical nature of the model is useful in that it parsimoniously penalizes violations of the two assumptions, with the strength of the penalty being determined by the data.
Gustafson
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Department of Statistics, University of British Columbia, Vancouver, Canada. gustaf@stat.ubc.ca
D001499:Bayes Theorem; D006801:Humans; D007902:Length of Stay; D015993:Life Tables; D008390:Markov Chains; D009010:Monte Carlo Method; D009735:Nursing Homes; D016016:Proportional Hazards Models; D016019:Survival Analysis