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We consider comparison of multiple endpoints among several independent populations. We extend O’Brien’s and Huang et al.’s methods from comparison of two groups to two more groups, and propose three max type test statistics, T1 based on normally distributed data, T2 obtained from pairwise ranking, and T3 derived from ranking of all populations. Numerical results show that all three test statistics maintain the desired type I error rates and achieve satisfactory power. When the normal assumption is justified, T1 is slightly more powerful than T2 and T3. However, when the normal assumption is violated, T2 and T3 gain sizable power. All three tests have higher power than O’Brien’s and Huang et al.’s methods using Bonferroni correction under the considered settings. The methods are exemplified using healthy eating index data from a study examining the conformance to dietary guideline.

Rank-based tests for comparison of multiple endpoints among several populations