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Simulation-based sample-sizing and power calculations in logistic regression with partial prior information.

Andrew P Grieve1, Shah-Jalal Sarker2

  • 1Adaptive Design Innovation Centre, Icon PLC, Marlow, UK.

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Summary
This summary is machine-generated.

Determining sample size for logistic regression models is challenging. Simulation offers a robust method for pharmaceutical development, addressing complexities in sample size calculations for improved accuracy.

Keywords:
convolutionlogistic regressionorthogonal polynomialssample sizingsimulation

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Area of Science:

  • Biostatistics
  • Pharmaceutical Sciences
  • Statistical Modeling

Background:

  • Existing methods for logistic regression sample sizing lack consensus.
  • Simulation is a powerful tool for complex adaptive designs in pharmaceutical research.
  • Accurate sample size determination is crucial for study power and reliable results.

Purpose of the Study:

  • To address challenges in using simulation for logistic regression sample size determination.
  • To present efficient methods for evaluating logistic functions and normal densities.
  • To introduce a heuristic approach for finding optimal sample sizes in simulations.

Main Methods:

  • Utilizing simulation to determine sample size for logistic regression.
  • Developing efficient algorithms for the convolution of logistic and normal distributions.
  • Implementing a heuristic search strategy for sample size optimization.
  • Illustrating the approach with three pharmaceutical case studies.

Main Results:

  • Demonstrated efficient simulation methods for logistic regression sample size calculations.
  • Validated a heuristic approach for sample size determination in complex scenarios.
  • Provided practical examples through pharmaceutical case studies.

Conclusions:

  • Simulation is an effective approach for determining sample size in logistic regression, particularly in pharmaceutical development.
  • The proposed methods enhance the efficiency and accuracy of simulation-based sample size calculations.
  • This work contributes to resolving the lack of consensus on optimal sample sizing techniques.