Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Yang comments on importance of migrant remittances to future of recipient families

Frey says America's black population is changing with recent immigration

Bailey and Danziger's War on Poverty book reviewed in NY Review of Books

Highlights

Cheng wins ASA Outstanding Graduate Student Paper Award

Hicken wins 2015 UROP Outstanding Research Mentor Award

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

Next Brown Bag

Mon, May 18
Lois Verbrugge, Disability Experience & Measurement

Bayesian Variable Selection with Joint Modeling of Categorical and Survival Outcomes: An Application to Individualizing Chemotherapy Treatment in Advanced Colorectal Cancer

Publication Abstract

Chen, W., D. Ghosh, Trivellore Raghunathan, and D.J. Sargent. 2009. "Bayesian Variable Selection with Joint Modeling of Categorical and Survival Outcomes: An Application to Individualizing Chemotherapy Treatment in Advanced Colorectal Cancer." Biometrics, 65(4): 1030-1040.

P>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.

DOI:10.1111/j.1541-0420.2008.01181.x (Full Text)

PMCID: PMC2870722. (Pub Med Central)

Country of focus: United States of America.

Browse | Search : All Pubs | Next