  The primary outcome of Randomized clinical Trials (RCTs) are typically
dichotomous, continuous, multivariate continuous, or time-to-event. However,
what if this outcome is unstructured, e.g., a list of variables of mixed types,
longitudinal sequences, images, audio recordings, etc. When the outcome is
unstructured it is unclear how to assess RCT success and how to compute sample
size. We show that kernel methods offer natural extensions to traditional
biostatistics methods. We demonstrate our approach with the measurements of
computer usage in a cohort of aging participants, some of which will become
cognitively impaired. Simulations as well as a real data experiment show the
superiority of the proposed approach compared to the standard in this
situation: generalized mixed effect models.
