Cambridge, England, United Kingdom
Volodymyr (Vladimir) Anisimov has an experience in pharmaceutical industry since 2002 when he joined the Research Statistics Unit, GlaxoSmithKline. Led the development and implementation of the innovative analytic statistical methodologies and tools for predictive patient recruitment and drug supply modelling in GSK trials. In 2012 joined the Advanced Analytics Group, Innovation, Quintiles. Since Oct 2015 worked as a Statistical Consultant in the area of predictive statistical modelling of various aspects of clinical trial operations for Pharma Industry. Honorary Professor in the School of Mathematics and Statistics, University of Glasgow, UK (2011-2018). In April 2018 joined Data Science, Center for Design & Analysis, Amgen Inc., London, UK. Working on the development and implementation of the innovative technologies for modeling/forecasting and optimal designing some operational characteristics in clinical trials, e.g. optimal design, forecasting and centralized statistical monitoring of patient recruitment, predicting event counts in event-driven trials (oncology),... Internationally recognized expert in statistical modelling of clinical trials. Prior to switching career to pharma industry, worked as university professor. Founded the Applied Statistics Department at Kiev University, Ukraine and served for more than 20 years as Professor & Head and also as Supervisor of Probabilistic-Statistical Laboratory at the same Department. PhD, ScD (Hab Dr), diploma of Professor in Applied Statistics. Published more than 200 papers (about 50 in clinical trials modelling) and several books in stochastic models, applied probability and statistics, supervised 24 Ph.D. theses, elected member of the National Academy of Sciences in Ukraine. Supervised numerous number of different industrial projects and some EU projects (Tempus, Intas). Specialties: Probability models, Mathematical and Applied Statistics, Modelling clinical trials (patient recruitment, randomization, drug supply, study design), Centralized statistical monitoring, Risk-based monitoring, Stochastic processes (Markov, semi-Markov, switching processes, hierarchic models), Queueing theory, Asymptotic techniques
Predictive statistical modelling of various aspects of clinical trial operations including patient enrolment, event's counts, risk-based monitoring, randomization issues, supply modelling, optimal trial design.