JSM 2011 Online Program

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Abstract Details

Activity Number: 339
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #301209
Title: Predicting Analysis Time in Event-Driven Clinical Trials with Event-Reporting Lag
Author(s): Jianming Wang*+ and Chunlei Ke and Qi Jiang and Charlie Zhang and Steve Snapinn
Companies: Novartis Oncology and Amgen Inc. and Amgen Inc. and Exelixis and Amgen Inc.
Address: One Health Plaza, East Hanover, NJ, 07936,
Keywords: Event-Driven ; Clinical Trial ; Analysis Time Prediction ; Bayesian
Abstract:

For a clinical trial with a time-to-event primary endpoint, the accrual of the events of interest during the course of the clinical trial may determine the timing of the analysis and provide useful information for the planning of resources and drug development strategies. It is of interest to predict the analysis time early and accurately. Currently available methods use either parametric or non-parametric methods to predict the analysis time based on accumulating information about enrollment, event and drop-out rates. However, these methods are usually based on the implicit assumption that the available data are complete at the time of performing prediction. This may not be true when it takes an uncertain amount of time to report an event to the database. As a consequence, the data will be incomplete at the time of performing prediction. Specifically, some patients without a reported event might have had an event that had not yet been reported. Ignoring this event-reporting lag could substantially impact the accuracy of the prediction. In this talk, we describe a general parametric approach to predict analysis time by incorporating event-reporting lag using a Bayesian method.


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