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artcat: Sample-size calculation for an ordered categorical outcome

Author

Listed:
  • Ian R. White

    (University College London)

  • Ella Marley-Zagar

    (University College London)

  • Tim P. Morris

    (University College London)

  • Mahesh K. B. Parmar

    (University College London)

  • Patrick Royston

    (University College London)

  • Abdel G. Babiker

    (University College London)

Abstract

We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional-odds model. artcat implements the method of Whitehead (1993, Statistics in Medicine 12: 2257–2271). We also propose and implement a new method that 1) allows the user to specify a treatment effect that does not obey the proportional-odds assumption, 2) offers greater accuracy for large treatment effects, and 3) allows for noninferiority trials. We illustrate the command and explore the value of an ordered categorical outcome over a binary outcome in various settings. We show by simulation that the methods perform well and that the new method is more accurate than Whitehead’s method.

Suggested Citation

  • Ian R. White & Ella Marley-Zagar & Tim P. Morris & Mahesh K. B. Parmar & Patrick Royston & Abdel G. Babiker, 2023. "artcat: Sample-size calculation for an ordered categorical outcome," Stata Journal, StataCorp LP, vol. 23(1), pages 3-23, March.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:1:p:3-23
    DOI: 10.1177/1536867X231161934
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