Health services research often is directed towards making small improvements in a number of outcomes that reflect many aspects of the patient’s life rather than a large improvement in a single well defined outcome. A researcher might choose five scales to measure different aspects of treatment outcomes and not expect any large treatment differences on any single outcome measure. O’Brien (1984) has proposed a nonparametric statistical procedure which is particularly well suited to this type of problem and that can result in considerable increases in statistical power. This paper will briefly review O’Brien’s pooled rank method and develop power calculations. A detailed power calculation example will be presented and discussed. Adding outcome variables where the treatment effect is small compared to the variability could reduce the power of the pooled rank test. The effect on power of adding poor outcome variables will be discussed.
Martin, Donald C.; Diehr, Paula; Koepsell, Thomas D.; and Fihn, Stephan D., "Multiple Outcomes in Health Services Research: Hypothesis Tests and Power" (October 1997). UW Biostatistics Working Paper Series. Working Paper 150.