Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, San Diego, CA, United States of America.
Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, United States of America.
PLoS One. 2024 Aug 26;19(8):e0305225. doi: 10.1371/journal.pone.0305225. eCollection 2024.
An N-of-1 observational design characterizes associations among several variables over time in a single individual. Traditional statistical models recommended for experimental N-of-1 trials may not adequately model these observational relationships. We propose an additive Bayesian network using a generalized linear mixed-effects model for the local mean as a novel method for modeling each of these relationships in a data-driven manner. We validate our approach via simulation studies and apply it to a 12-month observational N-of-1 study exploring the impact of stress on daily exercise engagement. We demonstrate the improved performance of the additive Bayesian network to recover the underlying network structure. From the empirical study, we found statistically discernible associations between reports of stress and physical activity on a population level, but these associations may differ at an individual level.
在一项单个人的研究中，n-of-1 观察性设计可以描述多个变量随时间的关联。推荐用于实验性 n-of-1 试验的传统统计模型可能无法充分模拟这些观察到的关系。我们提出了一种使用广义线性混合效应模型作为局部均值的加性贝叶斯网络，这是一种新颖的方法，可以以数据驱动的方式对这些关系中的每一个进行建模。我们通过模拟研究验证了我们的方法，并将其应用于一项为期 12 个月的探索应激对日常锻炼参与影响的单个人 n-of-1 观察性研究。我们证明了加性贝叶斯网络在恢复潜在网络结构方面的性能得到了提高。从实证研究中，我们发现了人群水平上报告的应激与身体活动之间存在统计学上可区分的关联，但这些关联在个体水平上可能有所不同。
