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Abstract Details

Activity Number: 674
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #307208
Title: A Bayesian Joint Survival Model for Predicting Treatment Efficacy via Quantitative MRI
Author(s): Jincao Wu*+ and Timothy D. Johnson
Companies: University of Michigan and University of Michigan
Address: Department of Biostatistics, Ann Arbor, MI, 48105,
Keywords: qMRI ; Bayesian ; Survival ; First Hitting Time
Abstract:

The prognosis for patients with malignant gliomas is poor with a median survival of 1 year. The assessment of therapy efficacy is unavailable until 0.5 years post diagnosis. Our work aims to assess therapy efficacy via quantitative MRI (qMRI) obtained 3 weeks after therapy starts. We propose a Bayesian joint survival model. In stage I, we smooth the qMRI via a pairwise difference prior and derive summary statistics. In stage II, we propose a Bayesian first hitting time (FHT) regression model. We model patients' health status as a latent Wiener process. Patients' survival time is modeled as the FHT to an absorbing state (i.e. death). We link the summary statistics derived in stage I to the parameters of the FHT via Bayesian hierarchical models. Through both simulation and real data analyses, our model provides an early assessment of the therapy efficacy and aid in personalized therapy.


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