Predictive Hierarchic Modeling of Operational Characteristics in Clinical Trials

Abstract

An analytic methodology for patient enrollment modeling using a Poisson-gamma model is developed by Anisimov & Fedorov (2005–2007). For modeling hierarchic processes associated with enrollment, a new methodology using evolving stochastic processes is proposed. This provides rather general and unified framework to describe various operational processes associated with enrollment. The technique for calculating predictive distributions, mean, and credibility bounds for evolving processes is developed. Some applications to modeling operational characteristics in clinical trials are considered with focus to modeling events associated with incoming and follow-up patients in different settings. For these models, predictive characteristics are derived in a closed form.

Keywords:

• Analytic modeling
• Clinical trials
• Evolving processes
• Patient enrollment
• Trial operation

Mathematics Subject Classification:

• Primary 60G55
• 60K99
• Secondary 92C50