  Clinical trial adaptation refers to any adjustment of the trial protocol
after the onset of the trial. The main goal is to make the process of
introducing new medical interventions to patients more efficient by reducing
the cost and the time associated with evaluating their safety and efficacy. The
principal question is how should adaptation be performed so as to minimize the
chance of distorting the outcome of the trial. We propose a novel method for
achieving this. Unlike previous work our approach focuses on trial adaptation
by sample size adjustment. We adopt a recently proposed stratification
framework based on collected auxiliary data and show that this information
together with the primary measured variables can be used to make a
probabilistically informed choice of the particular sub-group a sample should
be removed from. Experiments on simulated data are used to illustrate the
effectiveness of our method and its application in practice.

Machine Learning