Bayesian nonparametric Erlang mixture modeling for survival analysis

Yunzhe Li, Juhee Lee and Athanasios Kottas

Computational Statistics & Data Analysis, 2024, vol. 191, issue C

Abstract: Development of a flexible Erlang mixture model for survival analysis is introduced. The model for the survival density is built from a structured mixture of Erlang densities, mixing on the integer shape parameter with a common scale parameter. The mixture weights are constructed through increments of a distribution function on the positive real line, which is assigned a Dirichlet process prior. The model has a relatively simple structure, balancing flexibility with efficient posterior computation. Moreover, it implies a mixture representation for the hazard function that involves time-dependent mixture weights, thus offering a general approach to hazard estimation. Extension of the model is made to accommodate survival responses corresponding to multiple experimental groups, using a dependent Dirichlet process prior for the group-specific distributions that define the mixture weights. Model properties, prior specification, and posterior simulation are discussed, and the methodology is illustrated with synthetic and real data examples.

Keywords: Bayesian nonparametrics; Dependent Dirichlet process; Dirichlet process; Erlang distribution; Hazard function; Markov chain Monte Carlo; Survival analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:191:y:2024:i:c:s0167947323001858

DOI: 10.1016/j.csda.2023.107874

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