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Modeling individual migraine severity with autoregressive ordered probit models

机译：使用自回归有序概率模型对单个偏头痛的严重程度进行建模

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摘要

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. We take on the viewpoint of a patient who is interested in an individual migraine management strategy. Since factors influencing migraine can differ between patients in number and magnitude, we show how a patient's headache calendar reporting the severity measurements on an ordinal scale can be used to determine the dominating factors for this special patient. One also has to account for dependencies among the measurements. For this the autoregressive ordinal probit (AOP) model of Miiller and Czado (J Comput Graph Stat 14: 320-338, 2005) is utilized and fitted to a single patient's migraine data by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler. Initially, covari-ates are selected using proportional odds models. Model fit and model comparison are discussed. A comparison with proportional odds specifications shows that the AOP models are preferred.
机译：本文考虑了偏头痛严重程度评估建模的问题及其对天气和时间特征的依赖性。我们以对个体偏头痛治疗策略感兴趣的患者的观点为基础。由于影响偏头痛的因素在患者数量和数量上可能会有所不同，因此我们展示了如何按照序数尺度报告严重程度测量结果的患者头痛日历可用于确定该特殊患者的主要因素。还必须考虑测量之间的依赖性。为此，使用了Miiller和Czado的自回归序数位（AOP）模型（J Comput Graph Stat 14：320-338，2005），并通过组合移动多网格蒙特卡洛（GM-MGMC）Gibbs拟合了单个患者的偏头痛数据。采样器。最初，使用比例赔率模型选择协变量。讨论了模型拟合和模型比较。与比例赔率规格的比较表明，AOP模型是首选。

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