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Bayesian inference for recurrent events data using time‐dependent frailty
Author(s) -
Manda Samuel O. M.,
Meyer Renate
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1995
Subject(s) - deviance information criterion , covariate , random effects model , bayesian probability , proportional hazards model , statistics , computer science , econometrics , markov chain monte carlo , independent and identically distributed random variables , inference , context (archaeology) , deviance (statistics) , bayesian inference , mathematics , random variable , artificial intelligence , medicine , paleontology , meta analysis , biology
In medical studies, we commonly encounter multiple events data such as recurrent infection or attack times in patients suffering from a given disease. A number of statistical procedures for the analysis of such data use the Cox proportional hazards model, modified to include a random effect term called frailty which summarizes the dependence of recurrent times within a subject. These unobserved random frailty effects capture subject effects that are not explained by the known covariates. They are typically modelled constant over time and are assumed to be independently and identically distributed across subjects. However, in some situations, the subject‐specific random frailty may change over time in the same manner as time‐dependent covariate effects. This paper presents a time‐dependent frailty model for recurrent failure time data in the Bayesian context and estimates it using a Markov chain Monte Carlo method. Our approach is illustrated by a data set relating to patients with chronic granulomatous disease and it is compared to the constant frailty model using the deviance information criterion. Copyright 2004 © John Wiley & Sons, Ltd.