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.
Highlights
The calculation of the expected power of an experiment is a standard procedure often required by funding agencies, ethics boards or regulatory agencies
A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work
The power extrapolated from a specific study size was in a very good agreement with power curves obtained with the Monte Carlo-based power estimation (MCPE) algorithm
Summary
The calculation of the expected power of an experiment is a standard procedure often required by funding agencies, ethics boards or regulatory agencies. Power calculations for NLMEM are classically done by simulating a large number of datasets and re-estimating the simulated data with the planned analysis model to generate the distribution of the test statistic. This distribution is used to obtain a power estimate With this procedure, a large number of replicates is required for a stable estimate as each replicate contributes only dichotomous information (i.e., smaller or larger than the test threshold). A large number of replicates is required for a stable estimate as each replicate contributes only dichotomous information (i.e., smaller or larger than the test threshold) This process is especially time-consuming if the procedure is to be repeated for different study sizes to obtain full power versus study size curves (power curves)
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