Abstract: The incidence and prognosis of Head and Neck Cancer (HNC) depend heavily on patients’ Human PapillomaVirus (HPV) status. Prognosis analysis of HPV-associated HNC is of clinical importance because in-depth understanding of the survival distribution is valuable for designing more informed treatment strategies. In this article, we develop a novel Hierarchical Dirichlet Process Weibull Mixture Model (HDP-WMM) to study the prognosis of HNC patients given their HPV status. The HDP-WMM is capable of simultaneously characterizing the survival distributions of grouped data and capturing the dependence among different groups. Moreover, the HDP-WMM can identify clusters of patients based on their outcomes, providing additional information for exploring patient subtypes. Effective Markov chain Monte Carlo sampling algorithms are designed for model inference and function estimation. The clustering structure is identified by summarizing the posterior samples of the data random partition using the Bayesian cluster analysis tool. A simulation study is designed to validate the performance of the proposed inference methods. The practical utility of the proposed HDP-WMM is demonstrated by a case study on prognosis analysis of HPV-associated HNC. Our results show that the Bayesian HDP-WMM achieves satisfactory performance on estimating the survival functions and clustering patients based on their outcomes.

Keywords: Head and neck cancer; human papillomavirus; prognosis analysis; hierarchical Dirichlet process mixture model

该文2024年发表于IISE Transactions第8期。该期刊为AV淘宝 学术期刊分级方案A-级奖励期刊。

原文链接：//doi.org/10.1080/24725854.2023.2247047