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Bayesian analysis of multivariate survival data using Monte Carlo methods
Author(s) -
Aslanidou Helen,
Dey Dipak K.,
Sinha Debajyoti
Publication year - 1998
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315671
Subject(s) - markov chain monte carlo , multivariate statistics , bayesian probability , gibbs sampling , statistics , mathematics , monte carlo method , posterior probability , econometrics , computer science
This paper deals with the analysis of multivariate survival data from a Bayesian perspective using Markov‐chain Monte Carlo methods. The Metropolis along with the Gibbs algorithm is used to calculate some of the marginal posterior distributions. A multivariate survival model is proposed, since survival times within the same group are correlated as a consequence of a frailty random block effect. The conditional proportional‐hazards model of Clayton and Cuzick is used with a martingale structured prior process (Arjas and Gasbarra) for the discretized baseline hazard. Besides the calculation of the marginal posterior distributions of the parameters of interest, this paper presents some Bayesian EDA diagnostic techniques to detect model adequacy. The methodology is exemplified with kidney infection data where the times to infections within the same patients are expected to be correlated.