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Power and sample size for ordered categorical data

Department of Biostatistics, Harvard University of Public Health, Boston, USA, nrabbee{at}hsph.harvard.edu

B A Coull

Department of Biostatistics, Harvard University of Public Health, Boston, USA

C Mehta

Department of Biostatistics, Harvard University of Public Health, Boston, USA

N Patel

Cytel Software Corporation, Cambridge, USA

P Senchaudhuri

Cytel Software Corporation, Cambridge, USA

We propose a new method for computing power and sample size for linear rank tests of differences between two ordered multinomial populations. The method is flexible in that it is applicable to any general alternative hypothesis and for any choice of rank scores. We show that the method, though asymptotic, closely approximates existing exact methods. At the same time it overcomes the computational limitations of the exact methods. This advantage makes our asymptotic approach more practical for sample size computations at the planning stages of a large study. We illustrate the method with data arising from both proportional and non-proportional odds models in the two ordered multinomial setting.

Statistical Methods in Medical Research, Vol. 12, No. 1, 73-84 (2003)
DOI: 10.1191/0962280203sm317ra


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