A Bayesian Model for Detecting Acute Change in Nonlinear Profiles
检测非线性个体曲线急剧变化的贝叶斯模型AI 解读 ↓
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检测非线性个体曲线急剧变化的贝叶斯模型
- 研究问题
- 当纵向测量曲线缺乏简洁参数形式且个体差异较大时，如何平滑个体轨迹、定义响应变化并分析其与协变量的关系？
- 方法与思路
- 作者结合随机效应、非参数回归和动态状态空间模型平滑个体曲线，用状态参数定义变化，并通过层级模型及MCMC推断。
- 材料陈述的贡献
- 摘要称提供适用于复杂纵向曲线的层级建模框架，并提出可用于子模型后验模拟较容易的层级模型的MCMC算法。
- 阅读关注点
- 应核对状态参数如何定义急剧变化、平滑与变化检测的关系、协变量进入层级模型的方式，以及MCMC算法的适用条件。
- 阅读边界
- 依据OpenAlex索引摘要，稿件版本未注明；摘要未说明变化判定阈值、先验或算法收敛表现，动物实验背景不构成宏观结构变化证据。
原文摘要
We propose a model for longitudinal data with random effects that includes model-based smoothing of measurements over time. This research is motivated by experiments evaluating the hemodynamic effects of various agents in tumor-bearing rats. In one set of experiments, the rats breathed room air, followed by carbogen (a mixture of pure oxygen and carbon dioxide). The experimental responses are longitudinal measurements of oxygen pressure measured in tissue, tumor blood flow, and mean arterial pressure. The nature of the recorded responses does not allow any meaningful parametric form to model these profiles over time. Additionally, response patterns differ widely across individuals. Therefore, we propose a nonparametric regression to model the profile data over time. We propose a dynamic state-space model to smooth the data at the profile level. Using the state parameters, we formally define “change” in the measured responses. A hierarchical extension allows inference to include a regression on covariates. The proposed approach provides a modeling framework for any longitudinal data, where no parsimonious parametric model is available at the level of the repeated measurements and a hierarchical modeling of some feature of a smooth fit for these profiles data is desired. The proposed MCMC algorithm for inference on the hierarchical extension is appropriate in any hierarchical model in which posterior simulation for the submodels is significantly easier.