Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

ESM 数据的网络分析已在临床心理学中流行起来。在这种方法中，离散时间 (DT) 向量自回归 (VAR) 模型使用用于识别干预目标的中心性度量来定义网络结构。然而，VAR 模型受到时间间隔依赖性的影响。连续时间 (CT) 模型已被建议作为替代方案，但需要概念上的转变，这意味着 DT-VAR 参数反映的是总体效应而非直接效应。在本文中，我们提出并说明了一种使用 CT-VAR 模型的 CT 网络方法。我们定义了一个新的网络表示并开发了中心性措施，为干预目标提供信息。这种方法用一个 ESM 数据集来说明。