Bayesian prognosis analysis of human papillomavirus-associated head and neck cancer using hierarchical Dirichlet process mixture models

Ying Liao, Ning Dong and Yisha Xiang

IISE Transactions, 2024, vol. 56, issue 8, 855-869

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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2023.2247047 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:56:y:2024:i:8:p:855-869

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2023.2247047

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().