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- Title
Accelerating Monte Carlo power studies through parametric power estimation.
- Authors
Ueckert, Sebastian; Karlsson, Mats; Hooker, Andrew
- Abstract
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.
- Subjects
MONTE Carlo method; GAMES of chance; MATHEMATICAL models; ALGORITHMS; MATHEMATICAL programming
- Publication
Journal of Pharmacokinetics & Pharmacodynamics, 2016, Vol 43, Issue 2, p223
- ISSN
1567-567X
- Publication type
Article
- DOI
10.1007/s10928-016-9468-y