BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Ptof. Dr. Volodymyr V. Anisimov (Data Science Director\, Data Scie nce\, Center for Design & Analysis\, Amgen\, London\, UK) DTSTART:20260506T140000Z DTEND:20260506T150000Z DTSTAMP:20260512T105021Z UID:AMIS/5 DESCRIPTION:Title: Adv anced Data-Driven Statistical Technologies for Designing and Forecasting O peration in Late-stage Clinical Trials\nby Ptof. Dr. Volodymyr V. Anis imov (Data Science Director\, Data Science\, Center for Design & Analysis\ , Amgen\, London\, UK) as part of Asymptotic Methods in Statistics\n\n\nAb stract\nAbstract: Clinical trials in the modern era are characterized by t heir complexity and very high costs. With the need to recruit hundreds or even thousands of patients across multiple clinical sites and countries\, conducting efficient and effective trials has become a major challenge.\nD esigning and forecasting clinical trial operations remains one of the most pressing challenges in modern drug development\, with inefficient patient enrolment being a leading contributor to costly delays. \nThis talk prese nts recent advances in analytic and statistical methodologies aimed at imp roving the predictability and efficiency of clinical trial operation.\nWe introduce innovative data-driven technologies that are based on a rigorous and practical statistical framework (hierarchic stochastic models with ra ndom parameters) and enhance recruitment forecasting by accounting for key sources of uncertainty\, including variability in site activation timelin es\, heterogeneous enrolment rates across sites\, and temporal stochastici ty. These models enable dynamic\, stage-specific projections that better a lign operational plans with real-world trial behavior.\nA framework for op timizing cost-efficient recruitment strategies through intelligent site an d country selection is also presented. This methodology incorporates opera tional constraints such as regional enrolment caps and costs to balance fe asibility and resource allocation.\nInterim reforecasting approaches that leverage accumulating data to adaptively adjust recruitment plans are disc ussed with the goal of achieving the probability of meeting enrolment mile stones. Additionally\, statistical techniques for centralized monitoring a re introduced to identify atypical performance patterns\, flagging under- or over-performing sites and informing operational interventions.\nThe tal k also covers methods for forecasting key operational metrics critical to trial planning and oversight—such as projecting event accrual in oncolog y trials. \nThe utility of these approaches is demonstrated using various case studies that illustrate their application in complex\, global clinica l programs and show how these advanced tools are reshaping clinical trial operations\, cost management\, and ultimately improved outcomes.Collective ly\, these innovations can significantly improve trial predictability and efficiency and accelerate the drug development process.\nOur research work "Forecasting and cost-efficient designing restricted enrolment in clinica l trials" was recognized by the 2025 Award for Statistical Excellence in t he Pharmaceutical Industry from the Royal Statistical Society and PSI (UK) .\n LOCATION:https://researchseminars.org/talk/AMIS/5/ END:VEVENT END:VCALENDAR