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Abstract Details

Activity Number: 321
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305298
Title: Comparative Assessment of Joint Versus Conventional Modeling of Longitudinal and Survival Endpoints: A Reanalysis of P3 Oncology Data
Author(s): Mark Boye*+ and Joseph Ibrahim and Ming-Hui Chen and Ping Wang and Wei Shen and Danjie Zhang
Companies: 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
Address: 4557 Pascagoula Run, Greenwood, IN, 46143-6837, United States
Keywords: PRO ; joint-model ; survival ; longitudinal ; oncology ; PFS
Abstract:

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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