Random Effects Selection In Bayesian Accelerated Failure Time Model With Correlated Interval Censored Data

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Harun, Nusrat
Accelerated Failure Time Model; Bayesian Computation; Dirichlet Process Priors; Interval Censored Data; Mixture Distributions; Variable Selection; Biostatistics; Physical Sciences and Mathematics; Statistics and Probability
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DISS_para>In many medical problems that collect multiple observations per subject, the time to an event is often of interest. Sometimes, the occurrence of the event can be recorded at regular intervals leading to interval censored data. It is further desirable to obtain the most parsimonious model in order to increase predictive power and to obtain ease of interpretation. Variable selection and often random effect selection in case of clustered data becomes crucial in such applications. We propose a Bayesian method for random effects selection in mixed effects accelerated failure time models. The proposed method relies on Cholesky decomposition on the random effects covariance matrix and the parameter expansion method for the selection of random effects. The Dirichlet prior is used to model the uncertainty in the random effects. The error distribution for the AFT model has been specified using a Gaussian mixture to allow flexible error density and prediction of the survival and hazard functions. We demonstrate the model using extensive simulations and the Signal Tandmobiel Study®.