Abstract：In pharmaceutical/clinical development of a test drug or treatment, relevant clinical data are usually collected from subjects with the diseases under study in order to evaluate safety and efficacy of the test drug or treatment under investigation. For approval of pharmaceutical products (including drugs, biological products, and medical devices) in the United States (US), the Food and Drug Administration (FDA) requires that substantial evidence regarding the safety and effectiveness of the test treatment under investigation be provided in the regulatory submission (Section 314 of 21 CFR). Statistics plays an important role to ensure the accuracy, reliability, and reproducibility of the substantial evidence. Statistical methods and/or tools that are commonly used in the review and approval process of regulatory submissions are usually referred to as statistics in regulatory science or regulatory statistics. Thus, in a broader sense, statistics in regulatory science can be defined as valid statistics that is employed in the review and approval process of regulatory submissions of pharmaceutical products.

In this half-day workshop, I intend to discuss some practical issues that are commonly encountered in regulatory science for pharmaceutical development. These issues include, but are not limited to, (i) the use of a 90% confidence interval (CI) approach or a 95% CI approach for drug development, (ii) endpoint selection in clinical development, (iii) FDA’s current thinking in non-inferiority (similarity) margin selection, and (iv) sample size requirement. In addition, several clinical initiatives kicked off by FDA as the result of 21st Century Cure Act enacted by the US Congress in December 2016, which have presented major challenges to the pharmaceutical scientists and reviewers in regulatory review and approval process, will also be discussed. These major challenging issues include, but are not limited to, (i) complex innovative design (CID), (ii) rare diseases drug development, and (iii) other critical clinical initiatives such as precision/personalized medicine, model-informed drug development (MIDD), real-world data/evidence, and machine learning for imagine medicine (IM) and/or mobile individualized medicine (MIM).

Short Bio Sketch: Shein-Chung Chow, Ph.D. is currently a Professor at the Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA. In the past couple of years, Dr. Chow was on leave of absence for US Food and Drug Administration (FDA) as an Associate Director at Office of Biostatistics (OB), Center for Drug Evaluation and Research (CDER), FDA. Dr. Chow has been re-appointed as a Special Government Employee (SGE) by the FDA as an Advisory Committee voting member and Statistical Advisor to the FDA after his departure last April. Dr. Chow is the Editor-in-Chief of the Journal of Biopharmaceutical Statistics and the Editor-in-Chief of the Biostatistics Book Series at Chapman and Hall/CRC Press of Taylor & Francis Group. Dr. Chow an ASA (American Statistical Association) Fellow and an elected member of the ISI (International Statistical Institute). Dr. Chow has more than 30 years of experience in pharmaceutical/clinical development. He is the author or co-author of over 300 methodology papers and 30 books including Design and Analysis of Bioavailability and Bioequivalence Studies, Sample Size Calculations in Clinical Research, Design and Analysis of Clinical Trials, Adaptive Design Methods in Clinical Trials, Translational Medicine, Design and Analysis of Bridging Studies, Biosimilars: Design and Analysis of Follow-on Biologics,  Quantitative Methods for Traditional Chinese Medicine Development, and most recently Innovative Statistics in Regulatory Sciences.