Universally optimal crossover designs under subject dropout
The Annals of Statistics · 2013 · Volume 41 · Issue 1
Abstract
Subject dropout is very common in practical applications of crossover designs. However, there is very limited design literature taking this into account. Optimality results have not yet been well established due to the complexity of the problem. This paper establishes feasible, as well as necessary and sufficient conditions for a crossover design to be universally optimal in approximate design theory in 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.
Keywords
62J05, 62K05, Crossover designs, efficiency, robustness, subject dropout, universal optimality
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