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Sequential design for paired ordinal categorical outcome.

Baoshan Zhang1, Yuan Wu1

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.

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Summary

This study introduces a new method for analyzing grouped sequential clinical trials with ordinal outcomes, improving treatment effect assessment. The research offers a practical flowchart for sample size determination in sequential trial design.

Keywords:
Ordinal categorical dataU-statisticsWilcoxon signed-rank testsequential designstroke

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Ordinal Data Analysis

Background:

  • Clinical trials with ordinal outcomes often use grouped sequential designs.
  • Existing methods lack robust analysis for one-sample or paired ordinal data in sequential trials.
  • The modified Rankin Scale in stroke trials exemplifies the need for improved sequential analysis.

Purpose of the Study:

  • To develop a novel method for applying the Wilcoxon signed-rank test to grouped sequential designs for ordinal outcomes.
  • To provide a practical and theoretical framework for assessing treatment effects in such trials.
  • To enhance the design process of sequential clinical trials.

Main Methods:

  • Application of the Wilcoxon signed-rank test within a grouped sequential framework.
  • Derivation of variance formulas and demonstration of U-statistic asymptotic normality.
  • Validation through simulation studies and real data analysis.

Main Results:

  • Empirical Type I error rates and statistical power were validated.
  • The proposed method offers a practical and theoretical framework for ordinal outcome analysis.
  • A flowchart was developed to guide sample size determination for sequential trials.

Conclusions:

  • The novel method effectively addresses a critical gap in sequential trial design for ordinal outcomes.
  • The research provides tools for accurate treatment effect assessment and sample size planning.
  • This work enhances the efficiency and rigor of clinical trial design involving ordinal data.