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Accelerating Monte Carlo power studies through parametric power estimation
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)ORCID iD: 0000-0002-3712-0255
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)
2016 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 43, no 2, 223-234 p.Article in journal (Refereed) Published
Abstract [en]

Estimating the power for a non-linear mixed-effects model-based analysis is challenging due to the lack of a closed form analytic expression. Often, computationally intensive Monte Carlo studies need to be employed to evaluate the power of a planned experiment. This is especially time consuming if full power versus sample size curves are to be obtained. A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte Carlo simulation and estimations. The estimated parameter linearly scales with study size allowing a quick generation of the full power versus study size curve. A comparison of the PPE with the classical, purely Monte Carlo-based power estimation (MCPE) algorithm for five diverse pharmacometric models showed an excellent agreement between both algorithms, with a low bias of less than 1.2 % and higher precision for the PPE. The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the MCPE algorithm. PPE represents a promising approach to accelerate the power calculation for non-linear mixed effect models.

Place, publisher, year, edition, pages
2016. Vol. 43, no 2, 223-234 p.
Keyword [en]
Hypothesis test; Monte Carlo method; NONMEM; Non-linear mixed effect models; Power
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-287520DOI: 10.1007/s10928-016-9468-yISI: 000374704100008PubMedID: 26934878OAI: oai:DiVA.org:uu-287520DiVA: diva2:922879
Funder
EU, FP7, Seventh Framework Programme, 602552
Available from: 2016-04-25 Created: 2016-04-25 Last updated: 2017-11-30Bibliographically approved

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Ueckert, SebastianKarlsson, Mats O.Hooker, Andrew C
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