Bayesian variable selection with joint modeling of categorical and survival outcomes: An application to individualizing chemotherapy treatment in advanced colorectal cancer

Wei Chen, Debashis Ghosh, Trivellore E. Raghunathan, Daniel J. Sargent

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Colorectal cancer is the second leading cause of cancer related deaths in the United States, with more than 130,000 new cases of colorectal cancer diagnosed each year. Clinical studies have shown that genetic alterations lead to different responses to the same treatment, despite the morphologic similarities of tumors. A molecular test prior to treatment could help in determining an optimal treatment for a patient with regard to both toxicity and efficacy. This article introduces a statistical method appropriate for predicting and comparing multiple endpoints given different treatment options and molecular profiles of an individual. A latent variable-based multivariate regression model with structured variance covariance matrix is considered here. The latent variables account for the correlated nature of multiple endpoints and accommodate the fact that some clinical endpoints are categorical variables and others are censored variables. The mixture normal hierarchical structure admits a natural variable selection rule. Inference was conducted using the posterior distribution sampling Markov chain Monte Carlo method. We analyzed the finite-sample properties of the proposed method using simulation studies. The application to the advanced colorectal cancer study revealed associations between multiple endpoints and particular biomarkers, demonstrating the potential of individualizing treatment based on genetic profiles.

Original languageEnglish (US)
Pages (from-to)1030-1040
Number of pages11
JournalBiometrics
Volume65
Issue number4
DOIs
StatePublished - Dec 2009

Keywords

  • Bayesian multivariate regression
  • Biomarker
  • Hierarchical model
  • Interaction
  • Latent variable
  • Oncology

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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