gms | German Medical Science

Repeated measure designs and split plot plans are widely employed in scientific and medical research. The analysis of such designs is typically based on MANOVA models, requiring complete data, and certain assumption on the underlying parametric distribution such as normality or covariance matrix homogeneity. However, these methods are not applicable when discrete data or even ordered categorical data are present. In such cases, rank-based nonparametric methods are preferred. Several rank based nonparametric multivariate methods have been proposed in the literature. They overcome the distributional assumptions, but the issue with missing data remains. The aim of this work is to develop asymptotic correct procedures that are capable of handling missing values without assuming normality, and allowing for covariance matrices that are heterogeneous between groups and applicable for non-metric data, e.g. ordered categorical data. This is achieved by applying proper resampling methods. The asymptotic theory for our suggested approaches is methodologically validated. Their small sample performance is further studied in an extensive simulation study. Finally, an illustrative data example is analyzed.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.