This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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128
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #307731 |
Title:
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A Multistate Modeling and Prediction of Survival Distribution Using Information on Subject's Tumor Response Over Time
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Author(s):
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Shengyan Hong*+ and Iiya Lipkovich and Yan Daniel Zhao
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Companies:
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MedImmune and Eli Lilly and Company and Eli Lilly and Company
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Address:
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, , ,
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Keywords:
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Multi-state model ;
Bayesian survival analysis ;
tumor response ;
oncology trial
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Abstract:
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Prediction of overall survival in oncology clinical trials remains a challenging task and documented attempts to using progression-free survival as a surrogate endpoint had been unsatisfactory. We present a novel approach to predicting OS using tumor response over time via a multi-state transition model, where at any time a patient is categorized by one of four states: tumor response (tumor size decrease by certain %), tumor progression (tumor size increase by certain %), stable (neither response or progression), and death, and the transition probabilities from one of three tumor response states to death is estimated based on proportional hazards Cox model. We developed a Bayesian version of multi-state model to handle the problem of "sparse data" in state transitions. Our methods were evaluated via simulations and illustrated with a phase III clinical trial.
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