Mathematics - Colloquium
Tuesday, February 16, 2010
11:00 AM-12:00 PM
ABSTRACT: Case-control data analysis marks an important research area where statisticians have made significant contributions over the years. The fundamental problem in these studies is the comparison of a group of subjects having a particular disease (cases) to a group of disease free subjects (controls) with respect to some potential risk factors of the disease. In a typical case-control study, the exposure information is collected only once for the cases and controls. However, some recent medical studies have indicated that a longitudinal approach of incorporating the entire exposure history, when available, may lead to more precise estimates of the odds ratios of disease. In this work, we conduct a analysis of a case-control study when longitudinal exposure information is available for both cases and controls. We use semiparametric regression procedures to model the exposure profiles of the cases and controls and also the influence pattern of the exposure profile on the disease status. This enables us to analyze how the present disease status of a subject is influenced by his/her past exposure conditions conditional on the current ones. Analysis is carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo (MCMC) algorithms. The proposed methodology is motivated by, and applied to a case control study of prostate cancer where longitudinal biomarker information is available for the cases and controls. (This is joint work with Michael J. Daniels, Malay Ghosh, and Bhramar Mukherjee.)
Suggested Audiences: Adult, College
E-mail:
ma-chair@wpi.edu
Last Modified: February 5, 2010 at 10:51 AM
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