This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 402
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract - #308193
Title: Bayesian Semiparametric Analysis of Case-Control Studies with Time-Varying Exposures
Author(s): Dhiman Bhadra*+ and Michael J. Daniels and Malay Ghosh and Bhramar Mukherjee
Companies: University of Florida and University of Florida and University of Florida and University of Michigan
Address: 103 Griffin Floyd Hall, Gainesville, FL, 32611-8545,
Keywords: Bayesian Equivaence ; Case control study ; MCMC ; Odds ratio ; Penalized spline ; Semiparametric modelling
Abstract:

We present an analysis of a case-control study when longitudinally varying exposure information are available for the 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. 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 are available for the cases and controls. We conclude that, the entire exposure history, when incorporated, may lead to more precise estimates of the odds ratios of disease implying that the present disease status of a subject is often influenced by his/her past exposure conditions conditional on the current ones.


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