Adaptive allocation for binary outcomes using decreasingly informative priors.
A method of outcome-adaptive allocation is presented using Bayes methods, where a natural lead-in is incorporated through the use of informative yet skeptical prior distributions for each treatment group. These prior distributions are modeled on unobserved data in such a way that their influence on the allocation scheme decreases as the trial progresses. Simulation studies show this method to behave comparably to the Bayesian adaptive allocation method described by Thall and Wathen (2007), who incorporate a natural lead-in through sample-size-based exponents.
a Department of Biostatistics , Virginia Commonwealth University , Richmond , Virginia , USA.
Roy T
RT
Sabo
D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018401:Sample Size; D016008:Statistical Distributions
Adaptive randomization; Bayesian methods; Clinical trials