CALCULATION OF POWER AND SAMPLE-SIZE WITH BOUNDED OUTCOME SCORES

The two-sample Wilcoxon rank sum test is the most popular non-parametr ic test for the comparison of two samples when the underlying distribu tions are not normal. Although the underlying distributions need not b e known in detail to calculate the null distribution of the test stati stic, parametric assumptions are often made to determine the power of the test or the sample size. We encountered difficulties with this app roach in the planning of a recent clinical trial in stroke patients. I t is shown that, for power and sample size estimation, it can be dange rous to apply the classical formulae routinely, especially with outcom e scores having a U-shaped or a J-shaped distribution. As an example w e have taken the Barthel index, a quality-of-life outcome measure in s troke patients. Further, we have investigated alternative methods by m eans of Monte Carlo simulation. The distributional characteristics of the estimated powers were compared. Our findings suggest more appropri ate computer software is necessary for the calculation of power and sa mple size when efficacy is measured by a non-parametric method.