Cluster randomized trials are increasingly complex, often having multiple periods, multiple levels, or multi-component interventions. They include cohort and repeated cross-sectional samples in parallel, cluster crossover, and stepped wedge designs, respectively.

They span patient-centered outcomes, surveys, administrative, and electronic health records in a variety of settings including hospitals, jails, nursing homes, and primary medical practices—i.e., clusters—often targeting disadvantaged and vulnerable populations. They often include small numbers of clusters, resulting in challenges for valid design and statistical analysis.

CAPSL will build upon a recently completed PCORI-funded award that advances marginal modeling methods for stepped wedge and other complex cluster randomized trial designs.

TraCS supports cluster randomized trial requests that can be enhanced with novel CTS methods and tools developed by CAPSL. Tools include well-tested and user-friendly software to support the dissemination and implementation of novel methods.

Design and analysis methods for dynamic treatment regime estimation are essential ingredients of evidence-based precision health. These methods center on using data to create algorithms for assigning treatment to the right patient at the right time.

The designs address pragmatic concerns by seeking to reflect the structure of decision points in clinical care and public health policy as well as practical limitations and context. Such study designs include sequential multiple assignment randomized trials (SMARTs), micro-randomized trials for mHealth, observational study designs for precision health, and hybrid designs.

The main analytical tool for such data is off-policy reinforcement learning. CAPSL seeks to build on several recent research projects which arose out of TraCS research requests and collaborations to refine methodology for dynamic treatment regime estimation in a number of settings. These methods are included in the following projects:

• Design and analysis of the BEST (Biomarkers for Evaluating Spine Treatments) clinical trial—a two-stage SMART design that is part of the funded BACPAC study coordinated by UNC's Collaborative Studies Coordinating Center. BACPAC is a part of the NIH HEAL initiative.
• The HEDRA project—developing artificial intelligence methodology for real-time guidance of exercise in patients with type 1 diabetes. Research here involves off-policy reinforcement learning and real-time mHealth data.
• Use of electronic health record data from patients with Crohn's disease to develop dynamic treatment regime estimation to prolong time to second surgery.

TraCS supports such dynamic treatment regime estimation project-related requests that can be enhanced with novel design and reinforcement learning methods and tools developed by CAPSL. Tools include well-tested and user-friendly software to support the dissemination and implementation of novel methods.