 Potts-Cox survival regressionDanae Martinez-Vargas and Alejandro Murua-Sazo Computational Statistics & Data Analysis, 2023, vol. 187, issue C Abstract: A Bayesian semi-parametric survival regression model with latent partitions is introduced. Its goal is to predict survival and to cluster survival patients within the context of building prognosis systems. In order to drive cluster formation on individuals, the Potts random partition model is chosen as a prior on the covariates space. For any given partition, the proposed model assumes an interval-wise exponential distribution for the baseline hazard rate. The number of intervals is unknown. It can be estimated with a fused-lasso type penalty given by a sequential double exponential prior. Estimation and inference are done with the aid of Markov chain Monte Carlo. To simplify the computations, the Laplace's integral approximation method is used to estimate some constants and to propose parameter updates within Markov chain Monte Carlo. The methodology is illustrated with an application to cancer survival. Keywords: Approximate inference; Data augmentation prior; Mixture model; Prediction; Product partition model; Random cluster model; Semi-conjugate prior (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:187:y:2023:i:c:s0167947323001275 DOI: 10.1016/j.csda.2023.107816 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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