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artcat: Sample-size calculation for an ordered categorical outcome.

Ian R White1, Ella Marley-Zagar1, Tim P Morris1

  • 1MRC Clinical Trials Unit, University College London, London, U.K.

The Stata Journal
|May 8, 2023
PubMed
Summary
This summary is machine-generated.

A new statistical command, artcat, aids in sample size calculations for ordered categorical outcomes in clinical trials. It offers improved accuracy and flexibility over existing methods, enhancing research design.

Keywords:
artcatcategorical variableclinical trialevaluationnoninferiority trialpowerproportional-odds modelrandomized controlled trialsample sizest0700substantial-superiority trial

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Software

Background:

  • Sample size and power calculations are crucial for designing effective randomized controlled trials (RCTs).
  • Ordered categorical outcomes are common in clinical research but require specific analytical approaches.
  • Existing methods for sample size calculation with proportional odds models may have limitations.

Purpose of the Study:

  • To introduce a new Stata command, artcat, for sample size and power calculations in RCTs with ordered categorical outcomes.
  • To implement and evaluate a novel method for sample size calculation that relaxes the proportional-odds assumption and accommodates noninferiority trials.
  • To compare the utility of ordered categorical outcomes versus binary outcomes in various trial settings.

Main Methods:

  • The artcat command implements Whitehead's (1993) method for proportional odds models.
  • A new simulation-based method is proposed and implemented, allowing for non-proportional treatment effects and noninferiority margins.
  • The performance of both methods is assessed through simulations.

Main Results:

  • Simulations demonstrate that both implemented methods perform well.
  • The newly proposed method shows greater accuracy compared to Whitehead's method, particularly for large treatment effects.
  • Ordered categorical outcomes can offer advantages over binary outcomes in specific trial scenarios.

Conclusions:

  • The artcat command provides valuable tools for sample size and power calculations in trials with ordered categorical data.
  • The new method offers enhanced accuracy and flexibility, particularly beneficial for complex trial designs and large effect sizes.
  • Utilizing ordered categorical outcomes can lead to more efficient and informative clinical trial designs.