Recorded: May 13, 2021
The primary outcome of Randomized Control Trials (RCTs) are typically dichotomous, univariate discrete, continuous, or time-to-event. However, what if this outcome is a longitudinal sequence? That is, what if not only the amount, or rate of change of a key quantity are important, but also subtle changes in the covariance structure? When the outcome is unstructured, it is unclear how to assess RCT success and how to compute sample size. We show that kernel methods (e.g. MMD tests) 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 real data experiments, are presented.
Moderators: Ana Capuano, Michael Donohue
Panelist: Bruce M. Jedynak