Optimal Crossover Designs for Generalized Linear Models

• Published in: Journal of statistical theory and practice Vol. 14; no. 2
• Main Authors: Jankar, Jeevan, Mandal, Abhyuday, Yang, Jie
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2020
• Subjects: Algorithms; Analysis and Advanced Methodologies in the Design of Experiments; Mathematics and Statistics; Original Article; Probability Theory and Stochastic Processes; Statistical Theory and Methods; Statistics; Approximate designs; AR correlation structure; Two-stage design; optimality; Generalized estimating equations; Compound symmetric correlation
• ISSN: 1559-8608; 1559-8616
• DOI: 10.1007/s42519-020-00089-5

We identify locally D -optimal crossover designs for generalized linear models. We use generalized estimating equations to estimate the model parameters along with their variances. To capture the dependency among the observations coming from the same subject, we propose six different correlation structures. We identify the optimal allocations of units for different sequences of treatments. For two-treatment crossover designs, we show via simulations that the optimal allocations are reasonably robust to different choices of the correlation structures. We discuss a real example of multiple-treatment crossover experiments using Latin square designs. Using a simulation study, we show that a two-stage design with our locally D -optimal design at the second stage is more efficient than the uniform design, especially when the responses from the same subject are correlated.