Online Program Home
My Program

Abstract Details

Activity Number: 184
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #320798 View Presentation
Title: Robust Assessment of Trial Success Based on Co-Primary Endpoints by Bayesian and Bootstrapping Approaches with Discounting Option
Author(s): Zongjun Zhang* and Fanni Natanegara and Karen Price
Companies: Eli Lilly and Company and Eli Lilly and Company and Eli Lilly and Company
Keywords: Bayesian ; bootstrapping ; co-primary endpoints

Robust assessment of clinical trial success based on co-primary endpoints of historical data (such as phase II trials) is critical for internal decision making with respect to Phase III study design and plan. Two major sources of bias in early phase results are over-estimate of the true treatment effect and more homogenous patient population. We used Bayesian and bootstrapping approaches to keep the inherent correlation of the co-primary endpoints in modeling and assessment of clinical trial success in late phase. Discounting the treatment effect option is also implemented in both Bayesian and bootstrapping approaches in order to get robust assessment of clinical trial success in late phase. A motivating example is presented based on two virtual trials. The approaches can be applied to Single endpoint and extended to multiple endpoints in clinical trials.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association