Title: Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints,
Abstract: Adaptive designs for multi-armed clinical trials have become increasingly
popular recently in many areas of medical research because of their potential
to shorten development times and to increase patient response. However,
developing response-adaptive trial designs that offer patient benefit while
ensuring the resulting trial avoids bias and provides a statistically rigorous
comparison of the different treatments included is highly challenging. In this
paper, the theory of Multi-Armed Bandit Problems is used to define a family of
near optimal adaptive designs in the context of a clinical trial with a
normally distributed endpoint with known variance. Through simulation studies
based on an ongoing trial as a motivation we report the operating
characteristics (type I error, power, bias) and patient benefit of these
approaches and compare them to traditional and existing alternative designs.
These results are then compared to those recently published in the context of
Bernoulli endpoints. Many limitations and advantages are similar in both cases
but there are also important differences, specially with respect to type I
error control. This paper proposes a simulation-based testing procedure to
correct for the observed type I error inflation that bandit-based and adaptive
rules can induce. Results presented extend recent work by considering a
normally distributed endpoint, a very common case in clinical practice yet
mostly ignored in the response-adaptive theoretical literature, and illustrate
the potential advantages of using these methods in a rare disease context. We
also recommend a suitable modified implementation of the bandit-based adaptive
designs for the case of common diseases.