Non-parametric MANOVA approaches for non-normal multivariate outcomes with missing values

作者：He Fanyin; Mazumdar Sati; Tang Gong^*; Bhatia Triptish; Anderson Stewart J; Dew Mary Amanda; Krafty Robert; Nimgaonkar Vishwajit; Deshpande Smita; Hall Martica; Reynolds Charles F

来源：Communications in Statistics - Theory and Methods, 2017, 46(14): 7188-7200.

DOI：10.1080/03610926.2016.1146767

• Multivariate outcomes
• Kruskal-Wallis test
• missing data
• permutation

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the non-parametric multivariate Kruskal-Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete case analyses.

• 出版日期2017