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Power and sample size for ordered categorical data.

N Rabbee1, B A Coull, C Mehta

  • 1Department of Biostatistics, Harvard University School of Public Health, Boston, MA 02115, USA. nrabbee@hsph.harvard.edu

Statistical Methods in Medical Research
|March 6, 2003
PubMed
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This study introduces a new, computationally efficient method for determining sample sizes for linear rank tests in ordered multinomial data. The approach offers practical advantages for large study planning by overcoming limitations of existing exact methods.

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Methods

Background:

  • Ordered multinomial data analysis presents challenges in power and sample size computation.
  • Existing exact methods for sample size calculations can be computationally intensive and limited in scope.

Purpose of the Study:

  • To propose a novel, flexible, and computationally practical method for calculating power and sample size for linear rank tests.
  • To address the limitations of existing methods in the context of two ordered multinomial populations.

Main Methods:

  • Development of an asymptotic method for power and sample size calculations.
  • Application to linear rank tests for comparing two ordered multinomial distributions.
  • Flexibility to accommodate general alternative hypotheses and various rank scores.

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Main Results:

  • The proposed asymptotic method closely approximates existing exact methods.
  • The new method overcomes the computational limitations inherent in exact approaches.
  • Demonstrated applicability to both proportional and non-proportional odds models.

Conclusions:

  • The developed asymptotic method provides a practical and efficient tool for sample size determination in studies involving ordered multinomial data.
  • This approach is particularly beneficial for the planning stages of large-scale research projects.
  • The method's flexibility enhances its utility across diverse statistical modeling scenarios.