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An optimization approach to calculating sample sizes with binary responses.

Vahed Maroufy1, Paul Marriott, Hamid Pezeshk

  • 1a Department of Statistics and Actuarial Science , University of Waterloo , Waterloo , Ontario , Canada.

Journal of Biopharmaceutical Statistics
|April 5, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an optimization method for determining sample size in comparative studies by maximizing the expected net gain of sampling (ENGS). This approach enhances efficiency and reduces computational complexity compared to existing Bayesian methods.

Keywords:
Bayesian approachBinomial distributionDirichlet distributionENGSEVPIEVSIMonte Carlo methodSample sizeShannon information

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Area of Science:

  • Statistics
  • Biostatistics
  • Decision Science

Background:

  • Determining optimal sample size is crucial for efficient comparative studies.
  • Existing Bayesian methods for sample size optimization can be computationally intensive.
  • Value of information analysis provides a framework for decision-making under uncertainty.

Purpose of the Study:

  • To develop and present an optimization approach for sample size determination in comparative studies with binary responses.
  • To maximize the value of information by calculating the expected value of perfect information (EVPI).
  • To obtain the optimal sample size by maximizing the expected net gain of sampling (ENGS).

Main Methods:

  • The study utilizes a Bayesian framework with a Dirichlet prior for parameter estimation.
  • Data are assumed to follow two independent binomial distributions.
  • Monte Carlo integration is employed for computing and optimizing the expected net gain of sampling (ENGS).

Main Results:

  • The proposed method optimizes sample size by maximizing the expected net gain of sampling (ENGS).
  • The approach calculates the expected value of sample information (EVSI) and compares it to trial costs.
  • Computational complexity is significantly reduced compared to existing Bayesian methods.

Conclusions:

  • The presented optimization approach provides an efficient method for sample size determination in comparative studies.
  • Maximizing the expected net gain of sampling (ENGS) is a robust strategy for balancing information value and study costs.
  • This method offers a computationally advantageous alternative to traditional Bayesian approaches.