Universally Optimal Multivariate Crossover Designs

In this article, universally optimal multivariate crossover designs are studied. The multiple response crossover design is motivated by a \(3 \times 3\) crossover setup, where the effect of \(3\) doses of an oral drug are studied on gene expressions related to mucosal inflammation. Subjects are assigned to three treatment sequences and response measurements on \(5\) different gene expressions are taken from each subject in each of the \(3\) time periods. To model multiple or \(g\) responses, where \(g>1\), in a crossover setup, a multivariate fixed effect model with both direct and carryover treatment effects is considered. It is assumed that there are non zero within response correlations, while between response correlations are taken to be zero. The information matrix corresponding to the direct effects is obtained and some results are studied. The information matrix in the multivariate case is shown to differ from the univariate case, particularly in the completely symmetric property. For the \(g>1\) case, with \(t\) treatments and \(p\) periods, for \(p=t \geq 3\), the design represented by a Type \(\rm{I}\) orthogonal array of strength \(2\) is proved to be universally optimal over the class of binary designs, for the direct treatment effects.