CS1b: Concurrent - Exploring Best Practices: Case Studies, Collaborative Endeavors, and Evidence-Based Communication in Action

Recent reviews of the platform trial literature include hundreds of papers, yet case studies of real platform trials remain limited. Case studies are vital because they illustrate the concrete, unique challenges and opportunities presented to real-world platform trials. To this end, we propose a presentation on the Path to Prevention (P2P) phase 2 platform trial in Parkinson's disease. The P2P trial will investigate multiple therapies hypothesized to slow or prevent progression in Parkinson's disease gaining efficiency by using a common, perpetual trial infrastructure and sharing control participants, reinvesting saved resources into the investigation of new therapies. In addition to its platform efficiencies, the randomized P2P trial is nested within the Parkinson's Progression Marker Initiative (PPMI), an existing observational cohort of high-risk participants. This nesting allows P2P to leverage cohort data at both the population and individual level, as many people enroll in the PPMI cohort before enrolling in P2P.
The P2P platform uses two primary outcomes to explore treatment efficacy: a biomarker to measure change in dopamine transporter binding (mean striatal SBR) and a symptom scale to measure change in Parkinson's-related motor symptoms (MDS-UPDRS part III). Disease progression in each of these endpoints will inform treatment effect estimates within a disease progression modeling framework previously used successfully in Alzheimer's disease, ALS, and frontotemporal dementia. The trial's hierarchical primary analysis model combines information directly from its multiple therapeutic arms and indirectly from the broader PPMI study, incorporating the latest methods to handle possible time trends and augment platform trial control arms.
The P2P platform employs state of the art experimental design and modeling to efficiently advance the understanding and treatment of Parkinson's disease. Both P2P and PPMI are sponsored by the Michael J Fox Foundation. 

Ideas for projects can come in various forms and at different times in a career. Some ideas lead to projects that are not long-term, while others grow into a whole host of projects. In our careers, we have had ideas that have fit into these classifications, and everywhere in between. In this presentation, we consider a thread of an idea for producing a small suite of videos to use in training and mentoring the next generation of applied statisticians. That thread multiplied and has led us to a blanket of opportunities for further work in this arena. For example, the videos are now a part of a short course focused on navigating tough conversations in statistical collaboration. It also led to establishing new collaborations, broadening our own understanding of the field, and exciting ideas about new areas to explore. 

With the rise of evidence-based decision-making movements (e.g., in medicine and social policymaking), there is an increased need to communicate statistical evidence from research studies to decision-makers who may not have robust statistical and data literacy. We argue that instead of assuming research evidence will be used by and useful to decision-makers, we as statisticians and researchers should directly study strategies for disseminating statistical evidence and mobilizing knowledge. We delineate three areas of interconnected research that should be pursued to effectively study knowledge mobilization: (1) examining norms embedded in the statistical evidence we communicate, (2) descriptively understanding the statistical cognition of how decision-makers reason about this evidence, and (3) prescriptively developing and evaluating communication strategies that facilitate better use and usefulness of evidence. We demonstrate how this three-faceted framework – normative, descriptive, prescriptive – considers the perspectives and priorities of both researchers and decision-makers. As a case study, we present results from our recent statistical cognition experiment that evaluates four data visualizations used to communicate meta-analytic data to education policymakers and decision-makers. We point to existing evidence in education, data visualization, cognitive psychology, and human-computer interaction that should inform our statistical communication practices, and we identify areas where further knowledge mobilization research is needed.