论文标题
通用线性模型的最佳跨界设计
Optimal Crossover Designs for Generalized Linear Models
论文作者
论文摘要
我们为广义线性模型确定本地$ D $ - 最佳的跨界设计。我们使用广义估计方程来估计模型参数及其方差。为了捕获来自同一主题的观测值之间的依赖性，我们提出了六个不同的相关结构。我们确定不同处理序列的单位的最佳分配。对于两处理跨界设计，我们通过模拟显示，最佳分配对相关结构的不同选择是合理的。我们讨论了使用拉丁正方形设计进行多种治疗跨界实验的真实示例。使用仿真研究，我们表明，在第二阶段，具有本地$ D $最佳设计的两阶段设计比统一设计更有效，尤其是当相同的响应相关时。
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.
