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<p>This article aims to study efficient designs for crossover trials with multiple responses recorded from each subject in the time periods. A multivariate fixed effects model is proposed with direct and carryover effects corresponding to the multiple responses. The corresponding error dispersion matrix is chosen to be either of the proportional or the generalized Markov covariance type, permitting the existence of direct and cross-correlations within and between the multiple responses. The corresponding information matrices for direct effects under the two types of dispersions are used to determine efficient designs. The efficiency of orthogonal array designs of type <i>I</i> and strength 2 is investigated for a wide choice of covariance functions, namely, Mat(0.5), Mat(1.5) and Mat(<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\infty \)</EquationSource> </InlineEquation>). To motivate these multivariate crossover designs, a gene expression dataset in a <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(3 \times 3\)</EquationSource> </InlineEquation> framework is utilized.</p>

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Efficient designs for multivariate crossover trials

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Shubham Niphadkar,
  • Siuli Mukhopadhyay

摘要

This article aims to study efficient designs for crossover trials with multiple responses recorded from each subject in the time periods. A multivariate fixed effects model is proposed with direct and carryover effects corresponding to the multiple responses. The corresponding error dispersion matrix is chosen to be either of the proportional or the generalized Markov covariance type, permitting the existence of direct and cross-correlations within and between the multiple responses. The corresponding information matrices for direct effects under the two types of dispersions are used to determine efficient designs. The efficiency of orthogonal array designs of type I and strength 2 is investigated for a wide choice of covariance functions, namely, Mat(0.5), Mat(1.5) and Mat( \(\infty \) ). To motivate these multivariate crossover designs, a gene expression dataset in a \(3 \times 3\) framework is utilized.