论文标题
重复测量数据的贝叶斯非线性模型：概述，实现和应用程序
Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications
论文作者
论文摘要
当数据以来自感兴趣的人群的连续和重复测量的形式进行数据时，非线性混合效应模型已成为分析的标准平台，而受试者的时间概况通常遵循非线性趋势。虽然对非线性混合效应模型的频繁分析具有悠久的历史，但贝叶斯对模型的分析几乎没有关注直到1980年代后期，这主要是由于贝叶斯计算的耗时性。自1990年代初以来，该模型的贝叶斯方法开始出现以利用计算能力的快速发展，并且最近由于（1）优势量化了参数估计的不确定性，因此受到了极大的关注； （2）将先验知识纳入模型的实用性； （3）灵活性，以符合各种工业和学术领域引起的科学研究的复杂性的越来越多。这篇评论文章概述了建模策略，以实施非线性混合效应模型的贝叶斯方法，从设计现实生活问题的科学问题到实际计算。
Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a nonlinear tendency. While frequentist analysis of nonlinear mixed effects models has a long history, Bayesian analysis of the models has received comparatively little attention until the late 1980s due primarily to the time-consuming nature of Bayesian computation. Since the early 1990s Bayesian approaches for the models began to emerge to leverage rapid developments in computing power, and recently, have received significant attention due to (1) superiority to quantify the uncertainty of parameter estimation; (2) utility to incorporate prior knowledge into the models; and (3) flexibility to match exactly the increasing complexity of scientific research arising from diverse industrial and academic fields. This review article presents an overview of modeling strategies to implement Bayesian approaches for the nonlinear mixed effects models, ranging from designing a scientific question out of real-life problems to practical computations.
