基于反射布朗运动的反复事件模型及其在低血糖中的应用。

IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yingfa Xie, Haoda Fu, Yuan Huang, Vladimir Pozdnyakov, Jun Yan
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引用次数: 0

摘要

2型糖尿病患者需要密切监测血糖水平,作为糖尿病的常规自我管理。虽然许多治疗药物的目标是严格控制血糖,但低血糖往往是一个不良事件。在实践中,由于神经源性症状的感知,患者更容易观察到低血糖事件而不是高血糖事件。我们建议将每个患者观察到的低血糖事件建模为具有上反射屏障的反射布朗运动的下边界跨越事件。下限由临床标准确定。为了捕获患者异质性和患者内部依赖性,协变量和患者水平的脆弱性被纳入波动率和上反射屏障。该框架为潜在的血糖水平变异性、患者异质性和危险因素对血糖的影响提供了量化。我们利用马尔可夫链蒙特卡罗在贝叶斯框架上进行推理。模型选择采用了偏差信息准则和伪边际似然的对数两个模型比较准则。该方法在仿真研究中得到了验证。在分析DURABLE试验中糖尿病患者的数据集时,我们的模型提供了足够的拟合,生成的数据与观察到的数据相似,并提供了其他模型可能错过的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recurrent events modeling based on a reflected Brownian motion with application to hypoglycemia.

Patients with type 2 diabetes need to closely monitor blood sugar levels as their routine diabetes self-management. Although many treatment agents aim to tightly control blood sugar, hypoglycemia often stands as an adverse event. In practice, patients can observe hypoglycemic events more easily than hyperglycemic events due to the perception of neurogenic symptoms. We propose to model each patient's observed hypoglycemic event as a lower boundary crossing event for a reflected Brownian motion with an upper reflection barrier. The lower boundary is set by clinical standards. To capture patient heterogeneity and within-patient dependence, covariates and a patient level frailty are incorporated into the volatility and the upper reflection barrier. This framework provides quantification for the underlying glucose level variability, patients heterogeneity, and risk factors' impact on glucose. We make inferences based on a Bayesian framework using Markov chain Monte Carlo. Two model comparison criteria, the deviance information criterion and the logarithm of the pseudo-marginal likelihood, are used for model selection. The methodology is validated in simulation studies. In analyzing a dataset from the diabetic patients in the DURABLE trial, our model provides adequate fit, generates data similar to the observed data, and offers insights that could be missed by other models.

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来源期刊
Biostatistics
Biostatistics 生物-数学与计算生物学
CiteScore
5.10
自引率
4.80%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.
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