Predictive Hierarchic Modeling of Operational Characteristics in Clinical Trials

  • Vladimir V. Anisimov
  • Communications in Statistics - Simulation and Computation, October 2014, Taylor & Francis
  • DOI: 10.1080/03610918.2014.941488

Modeling of Operational Characteristics in Clinical Trials

What is it about?

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.

Why is it important?

Statistical design and trial operation are affected by stochasticity in patient enrollment and various event’s appearance. The complexity of clinical trials and multi-state hierarchic structure of various operational processes require developing new predictive analytic techniques for efficient modeling and predicting trial operation.


Prof Vladimir Anisimov
Amgen Inc

The developed methodology opens very wide opportunities for modelling various operational characteristics in clinical trials.

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The following have contributed to this page: Prof Vladimir Anisimov