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

AbstractThe 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 cancerhuman papillomavirusprognosis analysishierarchical Dirichlet process mixture model Data availability statementThe data that support the findings of this study are available in the Surveillance, Epidemiology, and End Results (SEER) Head and Neck with HPV Status Database (2010-2017) at https://seer.cancer.gov/seerstat/databases/hpv/index.html for users who already have access to the latest SEER Research Plus data with a valid institutional account and are allowed to have the access of this specialized database from the SEER custom data group via emails (seercustomdata@imsweb.com).Additional informationNotes on contributorsYing LiaoYing Liao is currently an assistant professor in Economics and Management School at Wuhan University, China. She received her PhD in Industrial Engineering from Texas Tech University, USA, in 2023. She also received her MS in Applied Statistics and BS in Statistics from Sun Yat-sen University, China. Her research interests include statistical machine learning, time series predictive analysis, and reliability modeling with applications to manufacturing and healthcare. She has published several articles in refereed journals, such as IISE Transactions, Naval Research Logistics, and Briefings in Bioinformatics. She was the recipient of the Doug Ogden Best Paper Award from the Society of Reliability Engineering in 2021 and the recipient of Doctoral Dissertation Completion Fellowship from Texas Tech University in 2022. She is a member of IISE and INFORMS. She was the founding president of INFORMS Student Chapter at Texas Tech University in 2021 and the IISE Future Faculty Fellow in 2022.Ning DongNing Dong is currently pursuing her PhD degree in the Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University. Her research interests include statistical learning for massive data in healthcare, and scalability of Markov chain Monte Carlo algorithms.Yisha XiangDr. Yisha Xiang is the associate professor of Industrial Engineering at the University of Houston. Her current research and teaching interests involves data-driven decision-making under uncertainty and statistical machine learning. Her research has been funded by National Science Foundation, including a CAREER grant, and industry. She has published articles in refereed journals, such as INFORMS Journal on Computing, IISE Transactions, European Journal of Operational Research, and Naval Research Logistics. She was the recipient of the P.K. McElroy award, Stan Oftshun award, and Doug Ogden award for best papers at the Reliability and Maintainability Symposium. Dr. Xiang received her BS in Industrial Engineering from Nanjing University of Aero. & Astro., China, and MS and PhD in Industrial Engineering from University of Arkansas. She serves as an Associate Editor for IISE Transactions and IEEE Transactions Automation Science and Engineering. She also currently serves as President of the IISE Quality Control and Reliability Engineering Division and Chair-elect of the INFORMS Quality, Statistics, and Reliability Sector. She is a member of IISE and INFORMS.