广义线性模型的最优交叉设计
Optimal Crossover Designs for Generalized Linear Models
摘要：
我们确定了广义线性模型的局部$D$ -最优交叉设计.我们使用广义估计方程来估计模型参数及其方差。为了捕捉来自同一主题的观测之间的依赖性，我们提出了六种不同的相关结构。我们确定了不同序列的治疗单位的最佳分配。对于两个治疗交叉设计，我们通过模拟表明，最佳分配是合理的相关结构的不同选择的鲁棒性。我们讨论了一个真实的例子，使用拉丁方设计的多处理交叉实验。使用模拟研究，我们表明，我们的本地$D$ -最优设计在第二阶段的两阶段设计是更有效的比均匀设计，特别是当从同一主题的响应是相关的。
英文摘要：
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.
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