Bayesian analysis of survival on multiple time scales.
We propose a Bayesian approach to the analysis of survival data on multiple time scales. Non-parametric modelling of variation of rates with more than one time scale is achieved using priors which specify smooth variation. Computations are conveniently carried out using Gibbs sampling. We discuss the extension of the method to Bayesian forecasting of rates. Numerical experience of two examples is described.
Berzuini
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C
Dipartimento di Informatica e Sistemistica, Università di Pavia, Italy.
Clayton
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D
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D015331:Cohort Studies; D005260:Female; D016027:Heart Transplantation / Q000401:mortality; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000401:mortality; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011183:Postoperative Complications / Q000401:mortality; D016016:Proportional Hazards Models; D012306:Risk; D016019:Survival Analysis; D015996:Survival Rate; D013997:Time Factors