Bratton, DJ; (2015) Design issues and extensions of multi-arm multi-stage clinical trials. Doctoral thesis , UCL (University College London).

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Abstract

The increasing cost of randomised controlled trials is hindering the rate at which new, effective therapies reach patients. To accelerate drug development, more efficient clinical trial designs are needed. One such design which has had success in speeding up the evaluation of therapies in cancer is the multi-arm multi-stage (MAMS) design. This particular design compares multiple new treatments against a control in a single trial, obviating the need for multiple two-arm studies, and ceases recruitment to poorly performing arms during the study. To further increase efficiency, interim assessments can be based on an intermediate outcome which is on the causal pathway to the primary outcome of the trial, thus allowing phases 2 and 3 of evaluation to be incorporated into a single, seamless design. The MAMS design was initially developed for trials in cancer where time to event outcomes are commonly used. To make it more widely applicable to other disease areas, we first extend the design to other types of outcome measure such as binary. The new designs are then applied to trials in tuberculosis --- a disease area with many new treatments currently in the clinical pipeline and which may therefore benefit from using more efficient trial designs. We then consider more general design issues such as familywise error rate and expected sample size and present calculations of both measures using simulation. Methods are developed for finding designs which have the desired overall operating characteristics and which are the most efficient under particular optimality criteria, known as admissible designs. Guidance is provided for choosing the number of stages and allocation ratio for a particular number of arms and we apply the methods developed in the thesis to existing and hypothetical MAMS trials. Throughout, Stata programs are created and updated to accommodate the use of the methods in practice.

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