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Universally Optimal Crossover Designs under Subject Dropout

Event Type
Statistics Department
165 Everitt
Mar 28, 2013   4:00 - 5:00 pm  
Wei Zheng (IUPUI)

Crossover designs are designs of experiments in order to compare

effects of different treatments by applying them to a number of subjects

during a sequence of periods. That means each subject will have repeated

measurements based on different treatments. Subject dropout is very common

in practical applications of crossover designs. However, there is very

limited literature of experimental design taking this into account.

Optimality results have not yet been well established due to complexity of

the problem. This paper establishes feasible necessary and sufficient

conditions for a crossover design to be universally optimal in approximate

design theory under the presence of subject dropout. These conditions are

essentially linear equations with respect to proportions of all possible

treatment sequences being applied to subjects and hence they can be easily

solved. A general algorithm is proposed to derive exact designs which are

shown to be efficient and robust.

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