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Determining sample size for complex studies is challenging. This study introduces the mlpwr R package for simulation-based power analysis using surrogate modeling to optimize study design and costs.

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Area of Science:

  • Statistical modeling
  • Empirical study design
  • Computational statistics

Background:

  • Determining appropriate sample size is a critical challenge in empirical study design.
  • Monte Carlo simulations are often required for power estimation in complex statistical models.
  • Existing methods may not efficiently optimize multiple design parameters or consider costs.

Purpose of the Study:

  • To introduce the R package mlpwr for simulation-based power analysis.
  • To demonstrate the use of surrogate modeling for optimizing study design parameters.
  • To facilitate cost-efficient study designs by balancing parameters and considering costs.

Main Methods:

  • Utilizing surrogate modeling within the mlpwr package for power analysis.
  • Performing simulation-based power estimations for various statistical models.
  • Applying the package to optimize multiple design parameters, such as participant and group numbers in multilevel modeling.

Main Results:

  • The mlpwr package provides a flexible tool for power analysis across diverse statistical models and study designs.
  • Surrogate modeling effectively guides the search for optimal study parameters to achieve desired power or meet cost constraints.
  • The package enables cost-efficient design by considering the cost of each parameter.

Conclusions:

  • The mlpwr R package offers a powerful approach to simulation-based power analysis and optimal study design.
  • Surrogate modeling enhances the efficiency and cost-effectiveness of planning empirical studies.
  • The package is applicable to various complex statistical models, including item response theory and multilevel modeling.