Hierarchical Bayesian Continuous Time Dynamic Modeling
Hierarchical Bayesian Continuous Time Dynamic Modeling
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DOI：
10.1037/met0000168
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发表时间：
2018-12-01
影响因子：
7
通讯作者：
Voelkle, Manuel C.
中科院分区：
文献类型：
--
作者：
Driver, Charles C.;Voelkle, Manuel C.
连续时间动态模型类似于流行的离散时间模型，如自回归交叉滞后模型，但通过使用随机微分方程可以准确地解释测量之间的时间间隔差异，并更简洁地指定复杂的动态。因此，它们提供了强大而灵活的方法来理解正在进行的心理过程和干预，并允许进行可变次数和不规则间隔的测量。然而，有限的发展已经发生了关于使用连续时间模型在一个完全分层的上下文中，其中所有的模型参数都允许不同的个人。这意味着，有关参数的个体差异的问题不得不依赖于单一受试者的时间序列方法，这需要每个人更多的测量场合。我们提出了一种分层贝叶斯方法来估计连续时间动态模型，允许所有模型参数的个体差异。我们还描述了一个扩展的ctsem包的R，它的接口斯坦软件，并允许简单的规范和拟合这样的模型。为了证明这种方法，我们使用的子样本从德国社会经济小组和整体生活满意度和满意度与health.Translational AbstractContinuous时间动态模型，使我们能够研究人们如何随着时间的推移而变化，以及如何在一个人的一个方面的变化，如健康，涉及到另一个方面的变化，如满意度。连续时间方面准确地考虑了测量之间的时间间隔的差异，并且因此允许以可变的次数和不规则的间隔进行测量。然而，有限的发展已经发生了关于使用连续时间模型，允许人们之间的差异，他们的动态系统的功能。这意味着，对于个体差异的问题，我们不得不依赖于为每个个体独立估计的模型，这需要对每个个体进行更多的测量。我们提出了一个层次贝叶斯方法来估计连续时间动态模型，它允许在各个方面的个人的动态系统和测量属性的个体差异。我们还描述了一个扩展的ctsem包的R，它的接口斯坦软件，并允许简单的规范和拟合这样的模型。为了证明这种方法，我们使用了德国社会经济小组的一部分，并将整体生活满意度和健康满意度联系起来。
Continuous time dynamic models are similar to popular discrete time models such as autoregressive cross-lagged models, but through use of stochastic differential equations can accurately account for differences in time intervals between measurements, and more parsimoniously specify complex dynamics. As such they offer powerful and flexible approaches to understand ongoing psychological processes and interventions, and allow for measurements to be taken a variable number of times, and at irregular intervals. However, limited developments have taken place regarding the use of continuous time models in a fully hierarchical context, in which all model parameters are allowed to vary over individuals. This has meant that questions regarding individual differences in parameters have had to rely on single-subject time series approaches, which require far more measurement occasions per individual. We present a hierarchical Bayesian approach to estimating continuous time dynamic models, allowing for individual variation in all model parameters. We also describe an extension to the ctsem package for R, which interfaces to the Stan software and allows simple specification and fitting of such models. To demonstrate the approach, we use a subsample from the German socioeconomic panel and relate overall life satisfaction and satisfaction with health.Translational AbstractContinuous time dynamic models allow us to examine how people change over time, and how changes in one aspect of a person, such as health, relate to changes in another aspect, such as satisfaction. The continuous time aspect accurately accounts for differences in time intervals between measurements, and as such allows for measurements to be taken a variable number of times, and at irregular intervals. However, limited developments have taken place regarding the use of continuous time models that allow for differences between people in terms of how their dynamic system functions. This has meant that for questions regarding individual differences, we have had to rely on models estimated independently for each individual, which require far more measurement occasions per individual. We present a hierarchical Bayesian approach to estimating continuous time dynamic models, which allows for individual variation in all aspects of the individuals' dynamic systems and measurement properties. We also describe an extension to the ctsem package for R, which interfaces to the Stan software and allows simple specification and fitting of such models. To demonstrate the approach, we use a portion of the German socio-economic panel and relate overall life satisfaction and satisfaction with health.