This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 470
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306420
Title: Forecasting Clinical Trial Enrollment: A Case Study
Author(s): Zachary Skrivanek*+
Companies: Eli Lilly and Company
Address: 546 S Meridian, Indianapolis, IN, 46225,
Keywords: adaptive design ; Bayesian forecasting ; predictive probability ; clinical trial ; Bayesian
Abstract:

Adaptive, seamless clinical trial designs can be more efficient than the traditional 2 phase, fixed design approach. Adapting the randomization scheme can improve dose selection. Adaptive randomization requires regulating the enrolment rate to be successful. A fast enrolment rate can undermine the potential of adaptive randomization. Monitoring enrolment and predicting future enrolment is important in managing the enrolment rate. In addition, this same methodology can be used to predict when milestone enrolment numbers will be achieved which can be key to successfully implementing an adaptive design, especially when it is seamless. The case study is a two stage seamless, adaptive design, with a burn-in period to accrue sufficient patients before adaptations began in the first stage. The predictions based on Bayesian forecasting were integral to the implementation of this trial.


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