Leveraging Real-World Data in Regulatory Clinical Trials: Leading with Statistical Innovation (Tentative)

Speaker: Lilly Yue, FDA

Lilly Yue, Ph.D. is Deputy Director, Division of Biostatistics in the FDA's Center for Devices and Radiological Health, where she oversees statistical reviews of therapeutic and diagnostic devices. She has played a key leadership role in advancing regulatory statistics, including a pioneering effort on adapting and advancing propensity score methodology for premarket observational studies and on developing and implementing the novel propensity score-integrated approaches for leveraging real-world evidence to support regulatory decision-making. Dr. Yue earned a BS in Mathematics and two MSs in Stochastic Operations Research and Statistics, respectively, and a Ph.D. in Statistics from Texas A&M University. She is an Editor-in-Chief of Pharmaceutical Statistics and served as an Associate Editor (and a Guest Editor) for Journal of Biopharmaceutical Statistics and Pharmaceutical Statistics. She is a Fellow of the American Statistical Association.

Abstract: High-quality real-world data (RWD) may be utilized to generate real-world evidence for medical product development and regulatory decisions. This presentation will focus on leveraging RWD in regulatory settings via a hybrid study in which RWD are used to (1) form a comparator group for a prospectively designed single-arm clinical study to approximate a traditional randomized controlled trial (RCT); (2) augment a prospectively designed single-arm study to approximate a traditional single-arm study of a larger sample size; or (3) augment a prospectively designed RCT to approximate a traditional RCT of a larger sample size. Novel propensity score-based methods will be discussed, which are used to design such clinical studies and deal with potential confounding bias introduced by leveraging RWD. A topic to be highlighted is the two-stage outcome-free study design framework for safeguarding the integrity of study design and interpretability of study results when leveraging RWD in regulatory clinical studies. Examples based on medical device pre-market regulatory review experience are provided to illustrate the implementation of these proposed methods. Finally, statistician’s role in leveraging RWD will be briefly discussed.

Title: Statistics in the real world: How statistics helps people with rare diseases

Speaker: Jane Larkindale, PepGen

Jane Larkindale is the Vice President of Clinical Science at PepGen, a company developing enhanced delivery oligonucleotide therapeutics with lead programs in Duchenne muscular dystrophy and myotonic dystrophy. She has dedicated the past 15 years of her career to accelerating therapy development for rare diseases, with a focus on neuromuscular diseases. She launched and ran international consortia and programs focused on data standardization and aggregation, and use of that data to support regulatory acceptance of disease models, outcome assessments and biomarkers to accelerate drug development. She conceived of and subsequently led the Rare Disease Cures Accelerator, Data and Analytics Platform and the Duchenne Regulatory Science Consortia, as well as multiple disease specific consortia at the Critical Path Institute. Prior to that, she initiated and led programs at the Friedreich’s Ataxia Research Alliance and the Muscular Dystrophy Association, all with the goal of accelerating rare disease drug development. These programs have built community consensus on best practices for drug development, and formal regulatory endorsement of several tools is underway. She launched and ran MDA Venture Philanthropy in 2007, which supported and guided 21 drug development projects through pre-clinical and early clinical phases of development, leaving MDA as the Vice President for Research in 2014. She went on to form NMD Consulting to support research and clinical strategy to accelerate drug development and worked with a multitude of for-profit and not-for-profit clients. She is a molecular biologist by training, having completed her D.Phil. (Ph.D.) in the Department of Plant Sciences at Oxford University, which she attended on a Rhodes Scholarship.

Abstract: There are more than 10,000 known rare diseases, defined by the US Food and Drug Administration (FDA) as conditions affecting fewer than 200,000 people in the US. Together, this means that rare diseases affect about 1 in 10 people, so while each one is rare, together they are common. Drug development for rare diseases is very challenging due to poor understanding of each disease, their symptoms and how the symptoms change over time, and because finding enough people to do clinical trials on can be very challenging. Regulatory Agencies such as the FDA cannot approve any new therapy without convincing evidence that the drug is both reasonably safe and reasonably likely to be effective, and that the potential benefit outweighs the risk. This can be challenging to demonstrate in small populations. However, innovative approaches to clinical development, including use of biomarkers, natural history models and innovative trial designs such as platform trials have allowed rare disease drug development to move forward.

Duchenne muscular dystrophy is a fatal X-linked degenerative disease that affects 1 in 10,000 live male boys at birth. People with Duchenne progressively lose muscle, lose the ability to walk, to raise their hands, to breathe and over time develop fatal cardiac issues. People with Duchenne have a life expectancy of about 28 years. Despite this poor prognosis, in the past 7 years 6 therapies have been approved for Duchenne, including 4 that are effective only in small subpopulations in the disease, and there are many more therapies in the pipeline. This innovation has been made possible in part through creative use of disease progression models to inform endpoint selection and to develop biomarkers and demonstrate how they relate to disease progression, and through novel trial designs. The use of these techniques to accelerate drug development for Duchenne and other rare diseases will be discussed.

Our Most Important Discovery: the Question

Speaker: Scott Evans, George Washington University

Dr. Scott Evans is a Professor and Founding Chair of the Department of Biostatistics Bioinformatics and the Director of The Biostatistics Center at George Washington University. He is the: Director of the Statistical and Data Management Center for the Antibacterial Resistance Leadership Group (ARLG) funded by NIAID/NIH; the PI of the Coordinating Center for the Exercise and Nutrition Interventions to Improve Cancer Treatment-Related Outcomes (ENICTO) in Cancer Survivors Consortium funded by the NCI/NIH, and the co-PI of the Data Coordinating Center of the Clamp OR Delay among neonates with Congenital Heart Disease (CORD-CHD) clinical trial funded by the NHLBI/NIH. He is the Co-Chair of the Benefit-Risk Balance for Medicinal Products Working Group of the Council for International Organizations of Medical Sciences (CIOMS); Editor of a mini-Series on DSMBs for the NEJM Evidence; and the President-elect of the Society for Clinical Trials (SCT). He is a recipient of the Mosteller Statistician Award, the Zackin Distinguished Collaborative Statistician Award, the Founders Award from the American Statistical Association (ASA), an elected member of the International Statistical Institute (ISI), and is a Fellow of the ASA, SCT, and the Infectious Disease Society of America (IDSA).

Abstract: Randomized clinical trials are the gold standard for evaluating the benefits and harms of interventions, though often fail to provide the practical evidence to inform medical decision-making. A primary reason is failure to recognize the most important questions for treating patients in clinical practice, and using this as the motivation for the design, monitoring, analysis, and reporting of clinical trials. Standard approaches synthesizing information obtained from separate marginal analysis of each outcome, fail to incorporate associations between outcomes and recognize the cumulative nature of outcomes in individual patients, suffer from competing risk complexities during interpretation of individual outcomes, and since efficacy and safety analyses are often conducted on different populations, generalizability of benefit-risk is unclear. A most promising opportunity is identifying and placing increased interest on questions of a pragmatic origin to match their clinical importance and making corresponding adjustments to the design and analysis of clinical trials. The utility of clinical trials is enhanced by: (i) using outcomes to analyze patients rather than patients to analyze the outcomes, and (ii) incorporating benefit:risk evaluation into standard trial design and conduct, rather than being conducted as a post-hoc exercise. The desirability of outcome ranking (DOOR), a new paradigm for the design, analysis, and interpretation of clinical trials based on a comprehensive patient-centric benefit- risk evaluation, is described to address these issues and advance clinical trial science.