A Bayesian adaptive design for clinical trials in rare diseases.

ABSTRACT: Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using the framework of finite-horizon Markov decision processes and dynamic programming (DP), a novel randomised response-adaptive design is proposed which maximises the total number of patient successes in the trial and penalises if a minimum number of patients are not recruited to each treatment arm. Several performance measures of the proposed design are evaluated and compared to alternative designs

SUBMITTER: Williamson SF 

PROVIDER: S-EPMC5473477 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature