Categorical Outcome Bayesian Utility Based (CATBUB) Designs
CATBUB designs use numerical utilities as a onedimensional comparator for
treatments whose performance is determined by categorical outcomes,
e.g., trinary, bivariatebinary, ordinal, etc.
Description
The software, i.e. an R program called "CATBUB_Design.R," contains design and analysis functions
catbub.design() and catbub.analysis() that facilitate the implementation of a group sequential CATBUB design.
This file is extensively annotated and explains the inputs and outputs for every function defined within.
Briefly, the design function implements the computational CATBUB design algorithms of Murray, Thall and Yuan [1].
The investigator supplies the targeted alternative and design parameters, i.e. alpha, beta, rho
and planned interim look sampling fractions, and the function returns a group sequential design,
i.e. planned maximum sample size, probability thresholds, and operating characteristics at the
supplied alternatives.
The analysis function allows the investigator to supply the observed data, planned maximum sample
size and previous interim sample sizes, and returns the decision, i.e. "Continue Enrollment", "A>B" or "B>A."
Features
 Written in R
 Computationally Efficient
 Easy to use
 Well documented
Illustrations
We also include illustrative R programs for trinary, bivariatebinary, bivariateordinal, and the CLL outcome.
We implement fixed sample, i.e. a single analysis, and group sequential designs for each outcome type.
For the trinary and bivariatebinary outcomes, we provide an example program for assessing sensitivity
to the elicited utilities.
For the trinary outcome, we provide an example of how to identify the relevant targeted alternative
among a set of targeted alternatives to use during the design.
System Requirements
 R v2.12.1 or newer, freelyavailable on CRAN
Credits
Thomas A. Murray implemented the numerical algorithms and authored the illustrative examples.
References
[1] Murray, Thomas A., Thall, Peter F., and Yuan, Ying (2016)
"UtilityBased Designs for Randomized Comparative Trials with Categorical Outcomes,"
Accepted to Statistics in Medicine
[2] Murray, Thomas A., Thall, Peter F., and Yuan, Ying (2016)
"Supplementary Materials: UtilityBased Designs for Randomized Comparative Trials with Categorical Outcomes,"
Accepted to Statistics in Medicine
