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Sample size calculations for ordered categorical data

J Whitehead1

  • 1Department of Applied Statistics, University of Reading, U.K.

Statistics in Medicine
|December 30, 1993
PubMed
Summary
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This study provides sample size formulas for analyzing ordered categorical data in clinical trials using logistic regression. It examines how category number and breadth affect analysis efficiency and discusses misclassification and stratification.

Area of Science:

  • Statistics
  • Clinical Trials
  • Biostatistics

Background:

  • Clinical trials frequently generate data on ordered categorical scales (e.g., very good, good, moderate, poor).
  • Analyzing such data often involves logistic regression under the proportional odds assumption.
  • For two-treatment comparisons, this logistic regression approach is equivalent to the Mann-Whitney test.

Purpose of the Study:

  • To derive sample size formulas specifically for logistic regression analysis of ordered categorical data.
  • To investigate the impact of the number and breadth of categories on statistical efficiency.
  • To discuss the effects of misclassification and stratification in these analyses.

Main Methods:

  • Derivation of sample size formulas consistent with logistic regression analysis.

Related Experiment Videos

  • Examination of the influence of category number and breadth on efficiency.
  • Discussion of misclassification and stratification effects with illustrative examples.

    • Main Results:

    • Development of novel sample size calculation methods for ordered categorical data.
    • Quantification of the efficiency trade-offs associated with different category structures.
    • Analysis of how misclassification and stratification impact sample size requirements.

    Conclusions:

    • The derived formulas provide a robust framework for sample size determination in studies with ordered categorical outcomes.
    • Understanding category design is crucial for optimizing statistical power and resource allocation.
    • The methods discussed offer practical guidance for researchers dealing with complex categorical data in clinical trials.