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Activity Number: 357
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315520 View Presentation
Title: A March Toward Efficient Clinical Trial Designs for Rare Endpoints: A Fixed Bayesian Design
Author(s): Yang Lei* and Susan Carlson and Byron J. Gajewski
Companies: and KUMC and University of Kansas Medical Center
Keywords: Rare endpoint ; Bayesian design ; Finite normal mixture model ; Simulation
Abstract:

This research was motivated by our goal to design an efficient clinical trial to compare two doses of docosahexaenoic acid supplementation for reducing the rate of earliest preterm births (gestation age< 34 weeks) and/or preterm birth (gestational age< 37 weeks). Dichotomizing continuous gestational age data and using classic binomial distribution will result in loss of information and reduced power. Informative priors can address this problem to a certain extent. Using distributional approach is also an improved strategy to dichotomize continuous data while retaining statistical power from continuous distribution. However, appropriate distributions that fit the data properly, particularly in the tails must be chosen. We proposed a three-component normal mixture model and introduced separate treatment effects at different components of gestational age. We evaluated operating characteristics of clinical trial designs comparing the finite mixture and beta-binomial models through simulation studies. We also applied the two methods to data from two clinical trials data, one in USA and one in Australia. Distributional approach turned out to be favorable in earliest preterm birth analysis.


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