This software simulates randomized trials in which the randomization
probabilities adapt in response to the outcome data. More patients are treated
with the better treatment while retaining the benefits of randomization. The
software supports both binary and time-to-event outcomes. Numerous design
options are supported.
The software supports up to 10 arms. Stopping rules may be based on a maximum
patient accrual or maximum trial length. Optionally one may specify a minimum
number of patients in the trial. Also, one may specify the number of
patients to randomize fairly before adaptive randomization begins. Stopping
rules are based on posterior probabilities.
The trial design contains a tuning parameter to control the degree to
which the randomization probabilities respond to data. If this parameter
is set to zero, the "adaptive" randomization is actually equal
randomization. The larger the value of this parameter, the more the
randomization favors what appears to be the better treatment. See the report
examining the role of this parameter on operating characteristics and giving
guidance for selecting its value.
See also  for a comparison of this tuning parameter to other methods of
controlling the randomization probabilities.
Randomization user's guide is available here and also included as part of the download.
See  for a study of the operating characteristics of adaptively
randomized clinical trials.
See Block ARAND for
adaptive randomization with blocks. See also our
Predictive Probability software interim analysis of randomized
See  for a description of the algorithm used for calculating
time-to-event randomization probabilities.
Algorithms for calculating the other randomization probabilities are described
If you are upgrading from a previous version of the software, please uninstall the previous version before installing the newer version.
For question concerning this software please contact
Biostatistics Software Support.
Odis Wooten and
J. Kyle Wathen developed the user interface using
C# and the Microsoft .NET framework version 2.0.
J. Kyle Wathen implemented the simulation kernel using Visual C++.
Thomas Liu and James Martin assisted in writing unit tests for the project.
 John D. Cook,
Understanding the Exponential Tuning Parameter in Adaptively Randomized
Trials (2006). UT MD Anderson Cancer Center Department of
Biostatistics Working Paper Series. Working Paper 27.
 John D. Cook,
Methods of Tuning Adaptively Randomized Trials (2007). UT MD
Anderson Cancer Center Department of Biostatistics Working Paper Series.
Working Paper 32.
 J. Kyle Wathen and John D. Cook.
Power and bias in adaptively randomized clinical trials (2006).
Technical Report UTMDABTR-002-06.
 Peter F. Thall and Jay K. Wathen,
Bayesian Adaptive Randomization in Clinical Trials (December 2006).
UT MD Anderson Cancer Center Department of Biostatistics Working
Paper Series. Working Paper 31.
 John D. Cook.
evaluation of gamma inequalities (2006). Technical Report
 John D. Cook Numerical Computation of Stochastic Inequality Probabilities
Technical report UTMDABTR-008-03