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Optimizing research design involves more than sample size. This study introduces a machine learning framework for efficient study design optimization, considering multiple parameters and costs.

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Area of Science:

  • Statistical modeling
  • Machine learning applications in research design

Background:

  • Adequately powered research design planning often requires more than sample size determination.
  • Complex scenarios necessitate simultaneous optimization of multiple design parameters, often relying on Monte Carlo simulations.
  • Cost-effectiveness is a critical factor in research design, aiming for desired power at minimal cost or maximal power within a budget.

Purpose of the Study:

  • To introduce a novel surrogate modeling framework utilizing machine learning predictions for optimizing research study designs.
  • To address complex optimization tasks involving multiple design dimensions and cost considerations where analytical solutions are unavailable.

Main Methods:

  • Development of a machine learning-based surrogate modeling framework.
  • Application of the framework to various hypothesis testing scenarios through a simulation study.
  • Demonstration of efficiency across single- and multidimensional design parameters.

Main Results:

  • The proposed framework efficiently solves complex study design optimization tasks.
  • Successfully demonstrated across diverse statistical models including t-tests, ANOVA, item response theory, multilevel models, and multiple imputation.
  • The framework handles multiple design dimensions and cost constraints effectively.

Conclusions:

  • The surrogate modeling framework offers an algorithmic solution for optimizing study designs, particularly when analytical power analysis is not feasible.
  • Provides a method to balance statistical power with cost considerations in research planning.
  • Publicly available R package 'mlpwr' facilitates the implementation of this optimization approach.