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

Fanyin He, Sati Mazumdar, Gong Tang, Triptish Bhatia, Stewart J. Anderson, Mary Amanda Dew, Robert Krafty, Vishwajit Nimgaonkar, Smita Deshpande, Martica Hall and Charles F. Reynolds

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 14, 7188-7200

Abstract: 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.

Date: 2017
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DOI: 10.1080/03610926.2016.1146767

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