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- W2055663307 abstract "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. Cet article a pour objet l'analyse de données de survie multivariées dans un perspective bayesienne utilisant des méthodes de chaǐne de Markov Monte Carlo. Metropolis, ainsi que l'algorithme de Gibbs est utilisé pour calculer les distributions marginales a posteriori. Nous proposons un modèle de survie multivariée puisque les temps de survie au sein d'un même “groupe” sont en corrélation en conséquence d'un effet de bloc aléatoire de fragillité. Le modèle des hasards conditionnels proportionnels de Clayton et Cuzick est utilisé avec un processus a priori structuré en martingale (Arjas et Gasbarra) pour le hasard de référence rendu discret. A part le calcul des distributions a posteriori marginales des paramètres d'intérêt, cet article présente certaines techniques bayesiennes de diagnostic EDA pour détecter l'insuffisance du modèle. La méthodologie est illustrée par des données sur l'infection du rein où Ton s'attend à ce que les temps avant les infections chez les meme patients soient en corrélation." @default.
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- W2055663307 title "Bayesian analysis of multivariate survival data using Monte Carlo methods" @default.
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- W2055663307 doi "https://doi.org/10.2307/3315671" @default.
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