Sample size calculation for an ordered categorical outcome

Ian White, Ella Marley-Zagar, Tim Morris, Mahesh K. B. Parmar and Abdel G. Babiker
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Ella Marley-Zagar: MRC Clinical Trials Unit at University College London
Tim Morris: MRC Clinical Trials Unit at University College London
Mahesh K. B. Parmar: MRC Clinical Trials Unit at University College London
Abdel G. Babiker: MRC Clinical Trials Unit, University College London

London Stata Conference 2020 from Stata Users Group

Abstract: We describe a new command, artcat, to calculate sample size or power for a clinical trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional odds model. The command implements an existing and a new method. The existing method is that of Whitehead (1993). The new method is based on creating a weighted data set containing the expected counts per person, and analysing it with ologit. We show how the weighted data set can be used to compute variances under the null and alternative hypotheses and hence to produce a more accurate calculation. We also show that the new method can be extended to handle non-inferiority trials and to settings where the proportional odds model does not fit the expected data. 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 are very similar when treatment effects are moderate. With very large treatment effects, the new method is a little more accurate than Whitehead’s method. The new method also applies to the case of a binary outcome and we show that it compares favourably with the official power and the community-contributed artbin

Date: 2020-09-11
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