In biomedical research,in order to evaluate the effect of a drug,investigators often need to compare the differences between one treatment group and another one by using multiple outcomes.The rank-sum tests can handle...In biomedical research,in order to evaluate the effect of a drug,investigators often need to compare the differences between one treatment group and another one by using multiple outcomes.The rank-sum tests can handle the case where the outcome differences between two groups are in the same direction.If they are not,MAX can handle it and is very useful when one/some of the differences is/are relatively larger than the others.When the individual outcome difference between two groups is moderate,a new method,summation of the absolute value of rank-based test for each outcome,is proposed in this work.Power comparison with the existing methods based on simulation studies and a real example show that the proposed test is a robust test,and works well when the difference for each outcome is moderate.The authors also derive some theoretical results for comparing the power between MAX and the the proposed method.展开更多
This paper proposes a new and distribution-free test called ＂Group Contingency＂ test （GC, for short） for testing two or several independent samples. Compared with traditional nonparametric tests, GC test tends to ...This paper proposes a new and distribution-free test called ＂Group Contingency＂ test （GC, for short） for testing two or several independent samples. Compared with traditional nonparametric tests, GC test tends to explore more information based on samples, and it＇s location-, scale-, and shapesensitive. The authors conduct some simulation studies comparing GC test with Wilcoxon rank sum test （W）, Kolmogorov-Smirnov test （KS） and Wald-Wolfowitz runs test （WW） for two sample case, and with Kruskal-Wallis （KW） for testing several samples. Simulation results reveal that GC test usually outperforms other methods.展开更多
基金partially supported by by the National Young Science Foundation of China under No.10901155the National Social Science Foundation of China under No.10CTJ004
文摘In biomedical research,in order to evaluate the effect of a drug,investigators often need to compare the differences between one treatment group and another one by using multiple outcomes.The rank-sum tests can handle the case where the outcome differences between two groups are in the same direction.If they are not,MAX can handle it and is very useful when one/some of the differences is/are relatively larger than the others.When the individual outcome difference between two groups is moderate,a new method,summation of the absolute value of rank-based test for each outcome,is proposed in this work.Power comparison with the existing methods based on simulation studies and a real example show that the proposed test is a robust test,and works well when the difference for each outcome is moderate.The authors also derive some theoretical results for comparing the power between MAX and the the proposed method.
基金This research is supported-by the National Natural Science Foundation of China under Grant No. 10731010 and Ph.D. Program Foundation of Ministry of Education of China under Grant No. 20090001110005.
文摘This paper proposes a new and distribution-free test called ＂Group Contingency＂ test （GC, for short） for testing two or several independent samples. Compared with traditional nonparametric tests, GC test tends to explore more information based on samples, and it＇s location-, scale-, and shapesensitive. The authors conduct some simulation studies comparing GC test with Wilcoxon rank sum test （W）, Kolmogorov-Smirnov test （KS） and Wald-Wolfowitz runs test （WW） for two sample case, and with Kruskal-Wallis （KW） for testing several samples. Simulation results reveal that GC test usually outperforms other methods.
