Speakers represent academia, industry and government agencies.
Dr. Jeff Barrett is Senior Vice-President at the Critical Path Institute serving as the Executive Director of the Rare Disease Cures Accelerator, Data Analytics Platform and a critical liaison between C-Path and the pharmaceutical industry, foundations, and other key stakeholders, helping grow C-Path’s portfolio in drug development solutions, with a focus, but not limited to model-informed drug development (MIDD) and real-world data (RWD) technologies. Jeff was previously Head of Quantitative Sciences at the Bill & Melinda Gates Medical Research Institute. In this role he was responsible for implementing model-based drug development, employing PK/PD modeling, statistics, and clinical trial simulations to advance the discovery and development of new medicines and vaccines. Prior to MRI, he was Vice President, of Translational Informatics at Sanofi Pharmaceuticals. He led various aspects of model-based decision-making spanning and provided leadership for Sanofi’s cloud-based, high-performance computing and “big data” initiatives. Jeff spent 10+ years at the University of Pennsylvania where he was Professor, Pediatrics and Director, Laboratory for Applied PK/PD at the Children’s Hospital of Philadelphia. Jeff received his B.S. in Chemical Engineering from Drexel University and Ph.D. in Pharmacokinetics from University of Michigan. He has co-authored over 180 manuscripts, is fellow of ACCP and AAPS and the recipient of numerous honors including ACCP awards for Young Investigator (2002) and Mentorship in Clinical Pharmacology (2007) and the AAPS Award in Clinical Pharmacology and Translational Research (2011). Dr. Barrett was awarded for Exceptional Innovation and Advancing the Discipline of Pharmacometrics at the International Society for Pharmacometrics (2013) and elected ISOP Fellow (2017). He is co-Specialty Chief Editor of Frontiers in Obstetric and Pediatric Pharmacology Journal and an active member of the Child Health and Human Development Pediatrics Subcommittee as a study section reviewer. He was a past acting chair of the FDA Advisory Committee for Pharmaceutical Science and Clinical Pharmacology; a voting member of the committee for 8 years.
Donald Berry is a professor in the Department of Biostatistics of the University of Texas M.D. Anderson Cancer Center. He was founding Chair of this department in 1999. Professor Berry received his Ph.D. in statistics from Yale University and has served on the faculty the University of Minnesota and Duke University. He has held endowed faculty positions at Duke and M.D. Anderson. Professor Berry has authored many books on biostatistics and over 400 peer-reviewed articles. He is a Web of Science Group Highly Cited Researcher and a Thomson Reuters Highly Cited Researcher in recognition of ranking among the top 1% of most cited researchers in Clinical Medicine. His Google Scholar H-index is 130. He has received numerous research grants from the NIH and NSF and is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Society of Bayesian Analysis. He has designed many innovative Bayesian adaptive clinical trials, with a primary focus in cancer. He is founder of Berry Consultants, a company that designs and has run innovative trials for pharmaceutical companies, medical device companies, NIH cooperative groups, patient advocacy groups, and international consortia.
Veronica Bunn is a Senior Manager in the statistical methodology group within the Statistical and Quantitative Sciences department of Takeda Pharmaceuticals. She is an experienced statistical study lead for global submissions and approvals in the Oncology setting. Her research interests include Bayesian borrowing methodologies, incorporation of RWE, and adaptive designs. She obtained her Ph.D. in Biostatistics from Florida State University.
Marc Buyse holds degrees from Brussels University (Belgium), Cranfield University (UK) and a ScD in biostatistics from the Harvard School of Public Health (USA). Prior to founding the International Drug Development Institute (IDDI) in 1991, he had worked at the European Organization for Research and Treatment of Cancer (EORTC) in Brussels and at the Dana Farber Cancer Institute in Boston. He is also the founder of CluePoints, a company dedicated to statistical monitoring of clinical trials, and an Associate Professor of biostatistics at the Limburgs Universitair Centrum, Diepenbeek, Belgium. He currently works on statistical methods for personalized medicine.
Abstract: A novel statistical approach to the analysis of randomized clinical trials (or other comparable patient series) uses all pairwise comparisons between two patients, one in the treatment arm and one in the control arm (Buyse 2010). Each pair favors treatment (“win”), control (“loss”), or neither. The “net treatment benefit” is the difference between the proportion of wins and the proportion of losses. Generalized pairwise comparisons (GPC) can incorporate several outcomes of interest and several thresholds of clinical relevance in the analysis, and as such, they can be used to personalize treatment choices and to assess the benefit/risk of randomized therapeutic interventions in a rigorous yet flexible manner (Buyse 2021).
One feature of GPC that is especially attractive for rare diseases is the possibility of increasing the power of the comparison between two groups of patients (e.g., a treated group and a control group) by analyzing several outcomes, either by prioritizing them from most important to least important, or by considering them simultaneously when categorizing pairs as wins or losses (O’Brien 1984). We used this feature in simulations to assess the operating characteristics of GPC to detect treatment effects on symptoms related to five clinically relevant domains (expressive language, daily living skills, gross motor, sleep and pain) in children treated for mucopolysaccharidosis type IIIA (Sanfilippo syndrome). The simulations showed that a sample of 50 patients, equally divided between a treated group and an intervention group, would be sufficient to detect clinically worthwhile treatment effects, whether the various domains of interest were prioritized, or considered simultaneously with equal weights (Deltuvaite-Thomas 2021).
Chenghao Chu received his Ph.D. in Biostatistics from the Indiana University-Purdue University at Indianapolis. He is currently an Associate Director of Biostatistics at Vertex Pharmaceuticals, working in the Cystic Fibrosis disease area. His research interest includes innovative study design and statistical analysis for rare disease drug development, and hypothesis testing for time-to-event data with non-proportional hazards.
Abstract: There are situations that clinical trials may incorporate historical data to more powerfully demonstrate the effectiveness of an experimental drug. In practice, it may be difficult to show historical data and current data are congruent. Borrowing incongruent historical data may result in estimation bias, Type-1 error rate inflation, and even reduced power. It remains a challenge for historical data borrowing methods to control Type-1 error rate inflation at an adequate level, while maintain sufficient power. We propose a method by using a weighted average of historical and current control data, with the weight dynamically determined by data. Simulation studies are conducted to compare the performance with existing methods.
Ruthie Davi is a Statistician and Vice President, Data Science at Acorn AI, a Medidata company, and has a background in pharmaceutical clinical trials with more than 20 years working as a Statistical Reviewer, Team Leader, and Deputy Division Director in the Office of Biostatistics in CDER at FDA. At Acorn AI Ruthie is part of a team creating analytical tools to improve the efficiency and rigor of clinical trials. Ruthie’s recent work is focused on creation and analysis of synthetic or external controls. Ruthie holds a Ph.D. in Biostatistics from George Washington University.
Anne Heatherington, PhD, is head of Takeda’s Data Science Institute and a member of the R&D senior leadership team. Dr Heatherington has more than 20 years of experience in the pharmaceutical and biopharmaceutical industries. Dr Heatherington is tasked with ensuring the company gets creative in how it brings its best people, technology and ideas together in unexpected ways to build and infuse a data and digital culture across R&D, including growing the company’s informatics capabilities in research; pioneering new approaches to modeling and simulation; and promoting learning through artificial intelligence. To work towards these goals, she is applying quantitative strategies in all aspects of drug development to drive innovation, efficiency and decision making across the organization.
Throughout her career, Dr Heatherington has led organizations and programs in large pharma, mid-size biotechs and startups. Before joining Takeda, Dr Heatherington worked as head of clinical development at Summit Therapeutics. She also spent 13 years at Pfizer, where she held several executive leadership roles, including vice president and head of quantitative clinical sciences. She earned her bachelor’s degree in pharmacy from Queen’s University Belfast in the United Kingdom and her doctorate degree in pharmacokinetics from the University of Manchester in England.
A practicing statistician for over 20 years, Glen Laird is currently the head of Biostatistics Methodology and Innovation at Vertex Pharmaceuticals, having previously led the GMA Biostatistics group at Vertex. Prior to his 4 years at Vertex, Glen worked in oncology biostatistics at Novartis, BMS, and Sanofi, assuming roles with increasing responsibility across early and full development. Glen graduated with a PhD in Statistics from Florida State University and worked as a survey statistician for RTI International before joining the pharmaceutical industry.
Jianchang Lin is a Director in Statistical and Quantitative Sciences (SQS) of Takeda Pharmaceuticals, has been leading a team to support a variety of development programs in oncology, immunology and rare disease. His team has supported implementation of numerous innovative trial designs and analyses across early and late phase development. He has extensive experience as statistical lead for several successful global submissions and approvals in US, EU, Japan and China. He has published over 40 statistical and clinical papers and served as editors for two books published in Springer. He is an Associate Editor of Journal of Biopharmaceutical Statistics. He also serves in program/steering committee for several conferences, e.g. ICSA Applied Statistics Symposium, ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop.
Xiaoyan Liu is a fifth-year Ph.D. student in Biostatistics at Boston University. He is the recipient of the Vertex Pharmaceuticals fellowship and has worked at Vertex as an extern in the biometrics department since 2017. He is currently working with Dr. Glen Laird on statistical issues in multiple disease areas.
Yan Wang is a statistical team leader in the Division of Biometrics IV, Office of Biostatistics in CDER at FDA, providing statistical leadership and support to the medical Division of Rare Disease and Medical Genetics since 2019. She has been at FDA for more than 15 years and played an active role in the development and application of statistical methodology used in the regulation of a variety of therapeutic areas including anti-infective, ophthalmology, transplant, and rare diseases. Prior to joining FDA, she worked in the pharmaceutical industry in the diabetic area for 5 years after receiving her PhD in Biostatistics from UCLA.
Abstract: Most randomized controlled clinical trials conducted in rare disease drug development include multiple endpoints to assess the effects of the drug on multiple clinical manifestations. While testing each endpoint separately is the paradigm for traditional clinical trial designs, this approach has lower power to detect a treatment effect when a trial has a small sample size and the treatment effect is not dramatic. In small sized trials, when an investigational drug is anticipated to have a clinically meaningful effect on multiple endpoints, it is desirable to perform a global test on the multiple endpoints so that a single probability statement (a p-value) can be made. Global tests focus on testing a global null hypothesis of no treatment effect on any of the multiple endpoints and are typically based on a combination of test statistics or measurements across multiple endpoints (O’Brien 1984; Ristl et al. 2018). In my talk, I will use examples and simulations to illustrate some desirable features of global tests for rare diseases, such as (1) increasing the power of statistically demonstrating a treatment effect and (2) the capability of providing a broader clinical benefit assessment for novel trials that evaluate patients based on varying efficacy endpoints due to heterogeneous clinical manifestations.
L.J. Wei is a professor of Biostatistics at Harvard University. Before joining Harvard, he was a professor at University of Wisconsin, University of Michigan, and George Washington University. His main research interest is in the clinical trial methodology, especially in design, monitoring and analysis of studies. He has developed numerous novel statistical methods which are utilized in practice. He received the prestigious Wald Medal in 2009 from the American Statistical Association for his contribution to clinical trial methodology. He is a fellow of American Statistical Associating and Institute of Mathematical Statistics. In 2014, to honor his mentorship, Harvard School of Public Health established a Wei-family scholarship to support students studying biostatistics. His recent research area is concentrated on translational statistics, the personalize medicine under the risk-benefit paradigm via biomarkers and revitalizing clinical trial methodology. He has more than 240 publications and served on numerous editorial and scientific advisory boards. L. J. Wei has extensive working experience in regulatory science for developing and evaluating new drugs/devices.
Jane Liang White is currently an Executive Director at Pfizer managing a group of statisticians in Oncology Hematology Franchise. Jane joined Pfizer Oncology in 2004 and has worked on numerous projects across Phase 1-4 with extensive submission experience in both solid tumors and hematology. Prior to Pfizer, Jane worked at Oncology Statistics of Bristol-Myers Squibb and as a financial analyst of Fidelity Investments. Jane received her Doctor of Science (ScD) in Biostatistics from Harvard University in 1996.
Abstract: Recent advances in health technology such as electronic health records (EHRs) enabled the gathering and utilization of Real-World Data (RWD) outside conventional randomized clinical trials (RCTs) to generate real-world evidence (RWE) which can potentially overcome limitations associated with RCTs. With the Food and Drug Administration (FDA) issuing the Framework for FDA’s RWE Program in December 2018 and the subsequent webinar by FDA on this framework in March 2019, RWD is poised to play a more important role in drug development and approvals. While generally less expensive and more representative of healthcare decisions in the real-world setting, RWE studies has its own set of challenges and require appropriate design and analyses. In this talk, I will discuss the lessons learned in drug applications involving RWD from both a successful drug approval and another one receiving unfavorable votes from the Oncology Drug Advisory Committee (ODAC); I will also share an exploration of a study design employing the RWD as the synthetic control arm aimed for the registration.
Jian Zhu is currently an Associate Director of Biostatistics at Servier Pharmaceuticals. He obtained his PhD in Biostatistics from University of Michigan, Ann Arbor and worked at Michigan Alzheimer's Disease Center and Takeda Pharmaceuticals prior to joining Servier. His drug development experience includes leading global oncology programs in hematologic malignancy and solid tumors, leading statistical innovation working groups and serving as statistical consultant in multiple therapeutic areas. His research interest includes adaptive designs, real world data, non-proportional hazards, Bayesian dose finding designs and missing data analysis. He has also published multiple papers on these topics in peer-reviewed journals and book chapters.
Abstract: Using RWD and other external data to supplement trial data is particularly relevant in rare diseases. On one hand, Bayesian methods have been proposed to borrow such external data; on the other hand, with more accessible individual patient level data, propensity score methods such as stratification have been used to specifically balance baseline characteristics and prognostic factors across data sources. We explored and generalized a framework combining propensity score stratification and Bayesian borrowing methods to improve the estimation of the current trial's parameter of interest. Various Bayesian methods were explored, including double hierarchical prior, robust mixture prior and power prior. Findings and conclusions based on an extensive simulation study pair-wisely comparing each method with its non-stratified counterpart will be discussed.