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Design Considerations for Vaccine Trials with a Special Focus on COVID-19 Vaccine Development
Jie Chen, MD, PhD
Biostatistics and Research Decision Sciences, Merck & Co., Inc.
Guest discussant: Jia Wang, PhD
R&D China Biometrics, AstraZeneca
The COVID-19 pandemic has triggered explosive activities in searching for cures, including vaccines against the SARS-CoV-2 infection. As of April 30, 2020, there are at least 102 COVID-19 vaccine development programs worldwide, the majority of which are in preclinical development phases, five are in phase I trial, and three are in phase I/II trial. Experts caution against rushing COVID-19 vaccine development, not only because the knowledge about SARS-CoV-2 is lacking (albeit rapidly accumulating), but also because vaccine development is a complex, lengthy process with its own rules and timelines. Clinical trials are critically important in vaccine development, usually starting from small-scale phase I trials and gradually moving to the next phases (II and III) after the primary objectives are met. This paper is intended to provide an overview on design considerations for vaccine clinical trials, with a special focus on COVID-19 vaccine development. Given the current pandemic paradigm and unique features of vaccine development, our recommendations from statistical design perspective for COVID-19 vaccine trials include: (1) novel trial design (e.g., master protocol) to expedite the simultaneous evaluation of multiple candidate vaccines or vaccine doses, (2) human challenge studies to accelerate clinical development, (3) adaptive design strategies (e.g., group sequential designs) for early termination due to futility, efficacy, and/or safety, (4) extensive modeling and simulation to characterize and establish long-term efficacy based on early-phase or short-term follow-up data, (5) safety evaluation as one of the primary focuses throughout all phases of clinical trials, (6) leveraging real-world data and evidence in vaccine trial design and analysis to establish vaccine effectiveness, and (7) global collaboration to form a joint development effort for more efficient use of resource and expertise and data sharing.
Jie is a Distinguished Scientist in Methodology Research at Merck Research Laboratories of Merck Sharp & Dohme (MSD). Jie received an M.D. in 1984 from Shanghai First College of Medicine, an MPH in 1994 in biostatistics & epidemiology from the University of Oklahoma Health Science Center, and a Ph.D. in 2003 in statistics from Temple University. Jie’s experience includes statistical methodology research and applications in non-clinical and pre-clinical research, clinical development, and post-licensure product life-cycle management. He has given short courses at FDA/Industry statistics workshop, EMA statistics symposium and many invited talks at academic institutions and statistical conferences. He has published a book on medical product safety and ~40 papers in peer-reviewed statistical and medical journals.