Next-generation SMARTs for Discovery and Evaluation of Sequential Cancer Therapeutic Strategies

Treatment of cancer is an ongoing process during which clinicians make a series of decisions at critical points in a patient's disease by synthesizing baseline and evolving patient information with the goal of optimizing expected long-term patient benefit. We use the term “treatment” to refer broadly to therapeutic agents and

supportive behavioral interventions to mitigate adverse effects of therapies or symptoms, as well as to inter- ventions focused on prevention and screening. An evidence-based approach to optimizing decision making is to study entire sequential treatment strategies, which can be formalized as treatment regimes. A treatment

regime is a sequence of decision rules, each of which is associated with a key decision and uses accrued information on a patient to select a treatment option from among the feasible options for the patient. An optimal regime is one that maximizes expected patient benefit in the population. Sequential multiple assign-

ment randomized trials (SMARTs), in which subjects are randomized at each of several key decision points to feasible treatment options based on their accrued information, are ideally suited to discovery and evaluation of treatment regimes, and a number of SMARTs in cancer have been conducted. At the same time, great

innovations have been made in cancer clinical trials; platform and response-adaptive trials that seek to op- timize treatment for both participants and future patients and that allow for incorporation of new options and elimination of ineffective options are increasingly being conducted. The potential for SMARTs to advance op-

timal sequential decision making in cancer treatment thus requires a next generation of design and analysis methods for SMARTs that incorporate similar innovations in the more complex setting of multiple decisions and repeated randomization of subjects and that address current cancer research priorities. The goal of this

project is to develop a comprehensive statistical framework for next-generation SMARTs in cancer research, the first steps toward which we will undertake through four specific aims. Our first aim is to develop methods for design and analysis of platform SMARTs that use response-adaptive randomization to favor optimal treatment

assignments and allow introduction of new treatments and discontinuation of ineffective treatments at any de- cision point. Aim 2 is to develop methods for design and analysis of SMARTs involving multi-component and multi-modal treatments at each decision point. Our third aim proposes a novel trial framework that merges a

SMART with a micro-randomized trial to allow joint optimization of sequential therapeutic decisions and selec- tion of supportive mHealth interventions that address the adverse consequences of cancer therapy, where the supportive interventions are chosen to maximize the success of therapy. In Aim 4, we develop a framework for

interim analysis of SMARTs, for which little methodology is available. The methods handle binary, continuous, and censored time-to-event outcomes of interest in cancer research. A software package will be developed to assist users in the design and analysis of next-generation SMARTs.