gms | German Medical Science

A high dimensional setting when the number of subjects is substantially smaller than the number of conditions to be tested is widely encountered in a variety of modern longitudinal and repeated measures design studies, with applications ranging from medicine to social sciences. Recently, several global testing procedures for high-dimensional repeated measures designs have been suggested. They can be employed to assess the global null hypothesis , e.g. of no global time effect. In statistical practice, however, the key question of interest is the identification of the significant factor levels, along with the computation of simultaneous confidence intervals for treatment effects. In this talk, we consider different resampling methods that can be used to derive multiple contrast tests and simultaneous confidence intervals for high dimensional designs. We discuss asymptotic properties of the proposed testing procedures and illustrate their finite-sample performance by simulations and case studies.

The authors declare that they have no competing interests.

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