Eric Rose (McGill University)
Title: Sample Size Calculations for Precision Medicine.
Abstract: There has been significant attention given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are usually gathered through longitudinal observational studies or through the use of Sequential Multiple Assignment Randomized Trials (SMARTs). These studies are typically sized for simple comparisons of fixed treatment sequences or in the case of observational studies sometimes not at all. We develop sample size procedures for the estimation of treatment regimes that ensure we have sufficient power for comparing the value of the optimal regime with standard of care and the value of the estimated optimal treatment regime is within a set range of the value of the true optimal regime with a high probability.