Predictive hierarchic modeling of operational characteristics in clinical trials

Anisimov, V. V. (2016) Predictive hierarchic modeling of operational characteristics in clinical trials. Communications in Statistics: Simulation and Computation, 45(5), pp. 1477-1488. (doi: 10.1080/03610918.2014.941488)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anisimov, Dr Vladimir
Authors: Anisimov, V. V.
College/School:College of Science and Engineering > School of Mathematics and Statistics
Journal Name:Communications in Statistics: Simulation and Computation
Publisher:Taylor & Francis
ISSN:0361-0918
ISSN (Online):1532-4141
Published Online:30 October 2014

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