Optimal Crossover Designs for Generalized Linear Models

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 str...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
ISSN:1559-8608
1559-8616
DOI:10.1007/s42519-020-00089-5