Universally optimal crossover designs under subject dropout

TL;DR

This paper addresses the challenge of subject dropout in crossover designs by establishing conditions for universal optimality, providing algorithms for efficient and robust exact design derivation.

Contribution

It introduces necessary and sufficient linear conditions for universal optimality in crossover designs with dropout, and proposes an algorithm for deriving efficient exact designs.

Findings

Conditions for universal optimality are linear and easily solvable.

The proposed algorithm produces efficient, robust exact designs.

Designs are validated to perform well under dropout scenarios.

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.