Multivariate many-to-one procedures with applications to preclinical trials

Abstract

Comparisons of several treatments with a control represent a standard situation in preclinical trials. Usually, they are considered with a single variable, resulting in multiple test procedures such as the Dunnett test (1). Here, the multivariate many-to-one problem is considered, where several variables are observed on each individual of the control and treatment groups. Classical MANOVA tests and their derivatives for the many-to-one problem require large sample sizes in order to be powerful if the dimension is high. In this paper, a new class of stabilized multivariate tests proposed by Läuter (2) and Läuter, Glimm, and Kropf (3) is extended to this special design. The new tests are based on linear scores which are derived in a certain way from the original variables. They utilize factorial relations among the variables. It is shown here that the procedures keep the multiple level. In simulation experiments several versions of multivariate tests are compared with each other. Standard approaches are included as well as different score versions and a comparison of Dunnett-like procedures with Bonferroni-type procedures. Generally, an improved power of the new tests compared to standard procedures is demonstrated. © 1997, Drug Information Association. All rights reserved.