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Analysis of crossover designs with multivariate response
Author(s) -
Grender Julie Myers,
Johnson William D.
Publication year - 1993
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780120108
Subject(s) - crossover , multivariate statistics , univariate , computer science , multivariate analysis , focus (optics) , statistics , mathematics , artificial intelligence , machine learning , physics , optics
Abstract Crossover designs involve observing the same response variate under different experimental conditions for each subject. Univariate methods are commonly used for analysis of data arising in these designs, but multivariate procedures offer a more general approach. The general multivariate linear model provides a natural framework for the simplest data structure as well as more complex settings with two or more response variates and measurements repeated over time. Multivariate models for crossover designs provide a unified approach that clarifies specification of hypotheses, assumptions required, and testing procedures in a wide class of applications that include longitudinal data as a special case. We focus on the 2 × 2 crossover design, but also describe models for analysing more complex crossover designs.