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Abstract Details
Activity Number:
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321
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #305298 |
Title:
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Comparative Assessment of Joint Versus Conventional Modeling of Longitudinal and Survival Endpoints: A Reanalysis of P3 Oncology Data
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Author(s):
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Mark Boye*+ and Joseph Ibrahim and Ming-Hui Chen and Ping Wang and Wei Shen and Danjie Zhang
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Companies:
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and The University of North Carolina at Chapel Hill and University of Connecticut and Eli Lilly and Company and Eli Lilly and Company and University of Connecticut
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Address:
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4557 Pascagoula Run, Greenwood, IN, 46143-6837, United States
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Keywords:
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PRO ;
joint-model ;
survival ;
longitudinal ;
oncology ;
PFS
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Abstract:
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Objective: Researchers include PROs in clinical trials to demonstrate from the patient's perspective the value of an investigational treatment. Data are collected as longitudinal repeated-measures and are often censored by occurrence of the survival event. Accordingly, joint models (JMs) can be estimated to provide information concerning the association of PRO items with survival; this approach can result in less biased and more efficient estimates of treatment effect, compared to separately-estimated outcomes. To facilitate JM use in organizations, we used frequentist analyses. Data: We consider the case in which longitudinal Lung Cancer Symptom Scale (LCSS) data, collected in "EMPHACIS" (Evaluation of MTA in Mesothelioma in a Phase 3 Study with Cisplatin) -also known as the Lilly Alimta JMCH trial- are jointly modeled with survival endpoints. Methods: Individual and joint models (e.g., two-step, random-effects, and direct-effects models) were fit to these data. We evaluated parameter estimates and fit statistics. Results: The magnitude and precision of parameter estimates varied by model specification. Some JMs did provide useful learning about the parameters of interest.
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