Clinical trials in the modern era are characterized by their 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.
Designing 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.
This talk presents recent advances in analytic and statistical methodologies aimed at improving the predictability and efficiency of clinical trial operation.
We introduce innovative data-driven technologies that are based on a rigorous and practical statistical framework (hierarchic stochastic models with random parameters) and enhance recruitment forecasting by accounting for key sources of uncertainty, including variability in site activation timelines, heterogeneous enrolment rates across sites, and temporal stochasticity. These models enable dynamic, stage-specific projections that better align operational plans with real-world trial behavior.
A framework for optimizing cost-efficient recruitment strategies through intelligent site and country selection is also presented. This methodology incorporates operational constraints such as regional enrolment caps and costs to balance feasibility and resource allocation.
Interim reforecasting approaches that leverage accumulating data to adaptively adjust recruitment plans are discussed with the goal of achieving the probability of meeting enrolment milestones. Additionally, statistical techniques for centralized monitoring are introduced to identify atypical performance patterns, flagging under- or over-performing sites and informing operational interventions.
The talk also covers methods for forecasting key operational metrics critical to trial planning and oversight—such as projecting event accrual in oncology trials.
The utility of these approaches is demonstrated using various case studies that illustrate their application in complex, global clinical programs and show how these advanced tools are reshaping clinical trial operations, cost management, and ultimately improved outcomes.Collectively, these innovations can significantly improve trial predictability and efficiency and accelerate the drug development process.
Our research work "Forecasting and cost-efficient designing restricted enrolment in clinical trials" was recognized by the 2025 Award for Statistical Excellence in the Pharmaceutical Industry from the Royal Statistical Society and PSI (UK).