Autologistic network model on binary data for disease progression study
Autologistic network model on binary data for disease progression study
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DOI：
10.1111/biom.13111
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发表时间：
2019-09
期刊：
影响因子：
1.9
通讯作者：
Yei Eun Shin;H. Sang;Dawei Liu;T. Ferguson;P. Song
中科院分区：
文献类型：
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作者：
Yei Eun Shin;H. Sang;Dawei Liu;T. Ferguson;P. Song
研究了具有吸收态的时空二值数据的分析问题。这项研究的动机是一项关于肌萎缩侧索硬化症（ALS）的临床研究，ALS是一种神经系统疾病，其特征是随着时间的推移，身体多个区域的肌肉力量逐渐丧失。我们提出了一个自体回归模型，以捕捉复杂的空间和时间依赖性，在不同的肌肉之间的肌肉力量。由于尚不清楚疾病如何从一个肌肉传播到另一个肌肉，因此基于空间邻近度来定义邻域结构可能不合理。放松在现有的模型中的空间邻域的预先指定的要求，我们的方法确定一个潜在的网络结构的经验来描述疾病传播的模式。该模型还允许网络自回归效应根据肌肉的先前状态而变化。基于该模型得到的联合分布，可以估计位置之间响应的联合转移概率，并可以预测下一个时间间隔内的疾病状态。模型参数通过惩罚伪似然最大化来估计。通过偏倚校正方法进行后模型选择推断，推导出渐近分布。仿真研究进行了评估所提出的方法的性能。该方法被应用于肌萎缩侧索硬化症临床研究的肌肉力量损失的分析。
This paper focuses on analysis of spatiotemporal binary data with absorbing states. The research was motivated by a clinical study on amyotrophic lateral sclerosis (ALS), a neurological disease marked by gradual loss of muscle strength over time in multiple body regions. We propose an autologistic regression model to capture complex spatial and temporal dependencies in muscle strength among different muscles. As it is not clear how the disease spreads from one muscle to another, it may not be reasonable to define a neighborhood structure based on spatial proximity. Relaxing the requirement for prespecification of spatial neighborhoods as in existing models, our method identifies an underlying network structure empirically to describe the pattern of spreading disease. The model also allows the network autoregressive effects to vary depending on the muscles’ previous status. Based on the joint distribution derived from this autologistic model, the joint transition probabilities of responses among locations can be estimated and the disease status can be predicted in the next time interval. Model parameters are estimated through maximization of penalized pseudo‐likelihood. Postmodel selection inference was conducted via a bias‐correction method, for which the asymptotic distributions were derived. Simulation studies were conducted to evaluate the performance of the proposed method. The method was applied to the analysis of muscle strength loss from the ALS clinical study.