Université de Paris, INSERM, IAME, Paris F-75006, France.
Université de Paris, INSERM, IAME, Paris F-75006, France.
Comput Methods Programs Biomed. 2021 Aug;207:106126. doi: 10.1016/j.cmpb.2021.106126. Epub 2021 May 4.
To optimize designs for longitudinal studies analyzed by nonlinear mixed effect models (NLMEMs), the Fisher information matrix (FIM) can be used. In this work, we focused on the multiplicative algorithms, previously applied in standard individual regression, to find optimal designs for NLMEMs.
We extended multiplicative algorithms to mixed models and implemented the algorithm both in R and in C. Then, we applied the algorithm to find D-optimal designs in two longitudinal data examples, one with continuous and one with binary outcome.
For these examples, we quantified the improved speed when C is used instead of R. Design optimization using the multiplicative algorithm led to designs with D-efficiency gains between 13% and 25% compared to non-optimized designs.
We found that the multiplicative algorithm can be used efficiently to design longitudinal studies.
为了优化通过非线性混合效应模型（NLMEM）分析的纵向研究设计，可以使用 Fisher 信息矩阵（FIM）。在这项工作中，我们专注于之前在标准个体回归中应用的乘法算法，以找到 NLMEM 的最优设计。
我们将乘法算法扩展到混合模型，并在 R 和 C 中实现了该算法。然后，我们将该算法应用于两个纵向数据示例中的 D-最优设计，一个是连续结果，另一个是二项结果。
对于这些示例，我们量化了使用 C 代替 R 时的改进速度。与非优化设计相比，使用乘法算法进行设计优化可使 D-效率提高 13%至 25%。
我们发现乘法算法可有效地用于设计纵向研究。
