A modified Monte Carlo mapped power approach was applied to evaluate the relationship between statistical power and various degrees of covariate effect sizes of the given study design.
The modified Monte Carlo mapped power approach was implemented successfully and enabled a numerical approach to evaluate the power to detect various degrees of food effects with the given study design.
This modified Monte Carlo mapped power approach may be a useful tool for evaluating the power to detect true covariate effects in mixed-effects modeling and a given study design.
An embedded functionality in PsN for rapid sample size calculations of mixed-effects models (24) was modified to calculate the minimum effect size of food on relative bioavailability needed for a statistically significant covariate relationship (modified Monte Carlo mapped power approach).
Another interesting tool for design evaluation, specifically for the calculation of study power and number of study subjects required, is Monte Carlo Mapped Power (mcmp).
The simulation-based power calculation methodology, based on the Monte Carlo Mapped Power method, enables the successful evaluation of mixed (part sparsely and part densely sampled) study designs.
