title: Accelerating Monte Carlo power studies through parametric power estimation.
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.
authors.affiliation: Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden. sebastian.ueckert@farmbio.uu.se.
authors.forename: Sebastian
authors.identifier: http://orcid.org/0000-0002-3712-0255
authors.initials: S
authors.lastname: Ueckert
authors.affiliation: Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.
authors.forename: Mats O
authors.initials: MO
authors.lastname: Karlsson
authors.affiliation: Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Hooker
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D018401:Sample Size
keywords: Hypothesis test; Monte Carlo method; NONMEM; Non-linear mixed effect models; Power