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28-11-2023

A Bayesian proportional hazards mixture cure model for interval-censored data

Authors: Chun Pan, Bo Cai, Xuemei Sui

Published in: Lifetime Data Analysis | Issue 2/2024

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Abstract

The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval-censored, the estimation of this model is challenging due to its complex data structure. In this article, we propose a computationally efficient semiparametric Bayesian approach, facilitated by spline approximation and Poisson data augmentation, for model estimation and inference with interval-censored data and a cure rate. The spline approximation and Poisson data augmentation greatly simplify the MCMC algorithm and enhance the convergence of the MCMC chains. The empirical properties of the proposed method are examined through extensive simulation studies and also compared with the R package “GORCure”. The use of the proposed method is illustrated through analyzing a data set from the Aerobics Center Longitudinal Study.

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Title: A Bayesian proportional hazards mixture cure model for interval-censored data
Authors: Chun Pan
Bo Cai
Xuemei Sui
Publication date: 28-11-2023
Publisher: Springer US
Published in: Lifetime Data Analysis / Issue 2/2024
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI: https://doi.org/10.1007/s10985-023-09613-8

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