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Optimal and maximin sample sizes for multicentre cost-effectiveness trials.

Md Abu Manju1, Math J J M Candel2, Martijn P F Berger2

  • 1Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands abu.manju@maastrichtuniversity.nl.

Statistical Methods in Medical Research
|February 7, 2015
PubMed
Summary

This study determines optimal sample sizes for multicentre trials comparing treatment cost-effectiveness. It provides formulas for maximin sample sizes, ensuring desired power at minimal research costs, crucial for evaluating net monetary benefit.

Keywords:
Cost-effectivenessmaximin designmulticentre trialsoptimal designpower

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Multicentre trials are essential for evaluating treatment cost-effectiveness.
  • Determining optimal sample sizes is critical for efficient resource allocation in research.
  • Assessing net monetary benefit requires robust statistical methodologies.

Purpose of the Study:

  • To derive optimal sample size calculations for multicentre trials.
  • To develop methods for determining sample sizes based on cost-effectiveness analysis.
  • To provide formulas for maximin sample sizes under uncertainty.

Main Methods:

  • Utilized a bivariate random-effects model to analyze costs and effects.
  • Considered fixed or random effects for centres and random effects for treatment-by-centre interactions.
  • Derived sample size formulas for optimal and maximin scenarios, including a worst-case evaluation.

Main Results:

  • Optimal sample sizes balance power and research costs.
  • Maximin sample size formulas guarantee a minimum power level at the lowest study costs.
  • Formulas are provided for calculating sample sizes to test treatment cost-effectiveness.

Conclusions:

  • The study offers a framework for optimizing sample sizes in cost-effectiveness trials.
  • Maximin sample sizes are valuable when model parameters are uncertain.
  • The derived formulas aid in planning efficient and powerful multicentre studies.