Vice President and Head of the Statistical Research and Consulting Center, Pfizer
Dr. Sandeep Menon is currently the Vice President and Head of the Statistical Research and Consulting Center at Pfizer Inc. and also holds adjunct faculty positions at Boston University and Tufts University School of Medicine. His group, located at different Pfizer sites globally, provides scientific and statistical leadership and consultation to the Global Head of Statistics and senior Pfizer management in discovery, clinical development, legal, commercial, and marketing. His responsibilities also include providing a strong presence for Pfizer in regulatory and professional circles to influence content of regulatory guidelines and their interpretation in practice. Previously he held positions of increased responsibility and leadership where he was in charge of all the biostatistics activities for the entire portfolio in his unit, spanning from discovery (target) through proof-of-concept studies for supporting immunology and autoimmune disease, inflammation and remodeling, rare diseases, cardiovascular and metabolism, and center of therapeutic innovation. He was responsible for overseeing biostatistical aspects of more than 40 clinical trials, more than 25 compounds, and 20 indications. He is a core member of the Pfizer Global Clinical Triad (Biostatistics, Clinical, and Clinical Pharmacology) Leadership team. He has been in the industry for over a decade, and prior to joining Pfizer he worked at Biogen Idec and Aptiv Solutions.
Sandeep is passionate about teaching and has been teaching part-time for over a decade. He has taught introductory, intermediate, and advanced courses in biostatistics, including adaptive designs in clinical trials. He has taught short courses internationally and is a regular invited speaker and panelist in academic, FDA/industry forums, and business management schools.
His research interests are in adaptive designs and personalized medicine. He has several publications in top-tier journals and recently coauthored and coedited Clinical and Statistical Considerations in Personalized Medicine and Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods. He is an active member of the Biopharmaceutical Section of the American Statistical Association (ASA), serving as associate editor of ASA journal Statistics in Biopharmaceutical Research (SBR) and as a core member of the ASA Samuel S. Wilks Memorial Medal Committee. He is the co-chair of the sub-team under the cross industry DIA-sponsored Adaptive Design Scientific Working Group (ADSWG) on the Role of Adaptive Designs in Personalized Medicine, member of the biomarker identification sub-team formed under the currently existing multiplicity working group sponsored by the Society for Clinical Trials, and an invited program committee member at the Biopharmaceutical Applied Statistics Symposium (BASS). He is on the editorial board for the Journal of Medical Statistics and Informatics and on the advisory board for the MS in biostatistics program at Boston University.
Sandeep received his medical degree from the University of Bangalore (formerly Karnataka University), India, and later completed his master's and PhD in biostatistics at Boston University. He was a research fellow at Harvard Clinical Research Institute. He has received several awards for academic and research excellence.
By This Author
Modern Approaches to Clinical Trials Using SAS®: Classical, Adaptive, and Bayesian Methods
This book thoroughly covers several domains of modern clinical trial design: classical, group sequential, adaptive, and Bayesian methods that are applicable to and widely used in various phases of pharmaceutical development.
- Marie Gaudard is a consultant specializing in statistical training with the use of JMP. She is currently a statistical writer with the JMP documentation team
- Satish Garla is a former Analytical Consultant in Risk Practice at SAS.
- Sam Gardner is a Senior Research Scientist at Eli Lilly and Company where he is focusing on business analytics and using statistical modeling.