Modern Analytic Techniques for Predictive Modeling of Clinical Trial Operations

What is it about?

The complexity of clinical trials and multi-state hierarchic structure of different operational processes require developing new predictive analytic techniques for efficient modeling and forecasting trial operation. The chapter is dealing with the investigation and further development of the following contemporary directions: modeling and forecasting patient enrollment at different levels using a Poisson-gamma model and its approximations; creating optimal enrollment design at start-up stage and interim adaptive enrollment adjustment accounting for time and cost constraints; monitoring interim trial enrollment performance and detecting atypical low/high enrolling sites/countries; forecasting event’s counts in event-driven trials and considering optimal design accounting for enrollment and follow-up stages; detection unusual event’s patterns in event-driven trials. For modeling more complicated hierarchic operational processes, a new methodology using evolving stochastic processes is also proposed. Some applications are considered. Keywords: Patient enrollment Poisson-gamma model Optimal design Interim trial performance Modeling event’s counts Modeling operational characteristics This is chapter in the book: Quantitative Methods in Pharmaceutical Research and Development, Olga V. Marchenko & Natallia V. Katenka (Eds), pp 361-408.

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