在移动健康数据中联合模型对纵向和复发事件结果的时间变化治疗效应估计
Estimation of Time-Varying Treatment Effects in a Joint Model for Longitudinal and Recurrent Event Outcomes in Mobile Health Data
AI总结：
本文提出基于联合纵向-生存模型的扩展方法，用于估计在微随机试验中反复施加治疗的效果，通过贝叶斯方法分析治疗与纵向结果及复发事件的关联，并展示模型选择和生存子模型校准的评估方法。
摘要：
移动健康技术不仅使研究人员能够追踪多个感兴趣的纵向结果的变化并记录随时间发生的健康相关事件，还允许将重复的低成本治疗实时送达个体。本文提出了一种基于联合纵向-生存模型的扩展方法，用于估计在微随机试验中反复施加治疗的效果。讨论了如何将这些重复治疗效果纳入联合模型的不同方式，这些模型规范对应于治疗对纵向和事件过程影响的不同机制。采用贝叶斯方法进行推断，模型治疗与多个纵向测量结果及复发事件的关联。还展示了如何计算信息标准用于模型选择，并呈现生存子模型校准的拟合度图。然后通过模拟和分析物质使用微随机试验的数据来展示方法的性能。
英文摘要：
Not only does mobile health technology enable researchers to track changes in multiple longitudinal outcomes of interest and to record the occurrence of health-related events over time, but it also allows for the delivery of repeated low-cost treatments directly to individuals in real time. We present a model-based approach for estimating the effect of repeatedly delivered treatments in a micro-randomized trial (MRT) via an extension of a joint longitudinal-survival model. We discuss different ways that these repeated treatment effects can be incorporated into the joint model; these different model specifications correspond to different mechanisms by which treatment is assumed to impact the longitudinal and event processes. Taking a Bayesian approach to inference, we model the association between repeated treatments, multiple longitudinally measured outcomes, and recurrent events. We also demonstrate how to calculate information criteria for model selection and present goodness-of-fit plots for assessing survival submodel calibration. We then illustrate the performance of our method via simulations and analysis of data collected in an MRT of substance use.
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