A Comprehensive Framework for Real-time Monitoring and Analysis Time Prediction in Clinical Trials

Music City Center 

Accurate prediction of key clinical trial milestones is crucial for efficient trial planning, particularly in event-driven studies. We propose a flexible framework integrating enrollment prediction, time-to-event modeling, and dropout estimation to project milestone events such as interim and final analyses. Our framework leverages Bayesian methodology for enrollment prediction, using a nonhomogeneous Poisson process with a quadratic time-varying rate function to model accrual dynamics. For event prediction, we incorporate piecewise exponential models with breakpoint estimation, enabling flexible hazard rate assumptions, alongside parametric mixture cure rate models, including exponential, Weibull, and Gompertz mixture cure models, to account for long-term survivors and non-proportional hazards. Additionally, we develop advanced time-to-dropout models with various distributional assumptions. Our simulation-based approach enhances analysis time estimation and outperforms conventional methods. Retrospective validation demonstrates substantial improvements in prediction accuracy. To support implementation, we developed an R Shiny application with an intuitive user interface.

Bayesian modeling

Event prediction

Mixture cure models

Clinical trial

Enrollment forecasting 

Biopharmaceutical Section