  Clinical trials are an integral component of medical research. Trials require
careful design to, for example, maintain the safety of participants, use
resources efficiently and allow clinically meaningful conclusions to be drawn.
Adaptive clinical trials (i.e. trials that can be altered based on evidence
that has accrued) are often more efficient, informative and ethical than
standard or non-adaptive trials because they require fewer participants, target
more promising treatments, and can stop early with sufficient evidence of
effectiveness or harm. The design of adaptive trials requires the
pre-specification of adaptions that are permissible throughout the conduct of
the trial. Proposed adaptive designs are then usually evaluated through
simulation which provides indicative metrics of performance (e.g. statistical
power and type-1 error) under different scenarios. Trial simulation requires
assumptions about the data generating process to be specified but correctly
specifying these in practice can be difficult, particularly for new and
emerging diseases. To address this, we propose an approach to design adaptive
clinical trials without needing to specify the complete data generating
process. To facilitate this, we consider a general Bayesian framework where
inference about the treatment effect on a time-to-event outcome can be
performed via the partial likelihood. As a consequence, the proposed approach
to evaluate trial designs is robust to the specific form of the baseline hazard
function. The benefits of this approach are demonstrated through the redesign
of a recent clinical trial to evaluate whether a third dose of a vaccine
provides improved protection against gastroenteritis in Australian Indigenous
infants.
