In this talk we review various of our contributions on sequential adaptive designs. Firstly, we review sequential targeted adaptive designs in which one adapts to complete data records of previously enrolled subjects, thereby relying on short-term clinical outcomes. In particular, we show how TMLE and online super-learning can be used to preserve unbiased formal inference in such adaptive designs. We demonstrate the power of this type of design for optimizing treatment for sepsis patients. Secondly, we discuss the natural extension of these sequential adaptive designs in continuous time in which case one adapts to all previously collected data, including many partially observed data structures of previously enrolled subjects. In this setting, we emphasize the utilization of time-specific surrogate outcomes as a way to still have power to learn powerful optimal rules while adapting over time the choice of surrogate so that we best approximate the optimal treatment rule w.r.t. the final clinical outcome. We show some simulation results demonstrating the performance of such adaptive designs in continuous time. Finally, we present sequential adaptive designs for a single time series in which one learns the optimal rule for maximizing the next short term outcome, again using TMLE and online super learning to allow for formal statistical inference. It is shown how all formal results rely on theory of TMLE and martingale processes.
This is a join work with: Antoine Chambaz, Wenjing Zheng, Ivana Malenica, Aurelien Bibaut, Aaron Hudson, Wenxin Zhang.