Bayesian hierarchical model for heterogeneity in the diagnosis and treatment of hepatocellular carcinoma (HCC) in the African continent Journal Article


Authors: Capanu, M.; Li, Y.; Chou, J.; Gönen, M.; Abou-Alfa, G. K.
Article Title: Bayesian hierarchical model for heterogeneity in the diagnosis and treatment of hepatocellular carcinoma (HCC) in the African continent
Abstract: HCC prevalence and etiology differ between African regions. To better understand these differences, a survey was conducted to characterize the health care practices in HCC management in Africa. Due to limited number of respondents from certain countries in Africa and due to scarce availability of resources, the traditional method of adaptive quadrature produces unstable estimates in this complex setting with sparse multinomial outcomes and with sparse clustered random effects. We propose a Bayesian hierarchical model to address this challenge, apply it to the sparse real data captured in the survey, and validate the proposed method through simulations on synthetic data. © 2024 Taylor & Francis Group, LLC.
Keywords: bayesian; hierarchical model; adaptive quadrature; clustered; multinomial; sparse random effects
Journal Title: Communications in Statistics Case Studies Data Analysis and Applications
Volume: 10
Issue: 3-4
ISSN: 2373-7484
Publisher: Taylor & Francis Group  
Date Published: 2024-01-01
Start Page: 302
End Page: 323
Language: English
DOI: 10.1080/23737484.2024.2434824
PROVIDER: scopus
PMCID: PMC12043323
PUBMED: 40308328
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF. Corresponding MSK author is Marinela Capanu -- Source: Scopus
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
  1. Yuelin Li
    220 Li
  2. Joanne Fu-Lou Chou
    332 Chou
  3. Mithat Gonen
    1030 Gonen
  4. Ghassan Abou-Alfa
    570 Abou-Alfa
  5. Marinela Capanu
    386 Capanu