Department of Public Health Sciences, The University of Chicago, Chicago, Illinois, USA.
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA.
Stat Med. 2022 May 10;41(10):1780-1796. doi: 10.1002/sim.9328. Epub 2022 Feb 9.
We address the issue of (non-) responsivity of self-initiated assessments in Ecological Momentary Assessment (EMA) or other mobile health (mHealth) studies, where subjects are instructed to self-initiate reports when experiencing defined events, for example, smoking. Since such reports are self-initiated, the frequency and determinants of nonresponse to these event reports is usually unknown, however it may be suspected that nonresponse of such self-initiated reports is not random. In this case, existing methods for missing data may be insufficient in the modeling of these observed self-initiated reports. In certain EMA studies, random prompts, distinct from the self-initiated reports, may be converted to event reports. For example, such a conversion can occur if during a random prompt a subject is assessed about the event (eg, smoking) and it is determined that the subject is engaging in the event at the time of the prompt. Such converted prompts can provide some information about the subject's non-responsivity of event reporting. Furthermore, such non-responsivity can be associated with the primary longitudinal EMA outcome (eg, mood) in which case a joint modeling of the non-responsivity and the mood outcome is possible. Here, we propose a shared-parameter location-scale model to link the primary outcome model for mood and a model for subjects' non-responsivity by shared random effects which characterize a subject's mood level, mood change pattern, and mood variability. Via simulations and real data analysis, our proposed model is shown to be more informative, have better coverage of parameters, and provide better fit to the data than more conventional models.
我们解决了自我启动评估（EMA）或其他移动健康（mHealth）研究中自我启动评估的反应性（无反应性）问题，在这些研究中，受试者被指示在经历定义的事件时（例如吸烟）自行报告。由于这些报告是自我启动的，因此通常不知道对这些事件报告的无反应频率和决定因素，但是可能怀疑这种自我启动报告的无反应不是随机的。在这种情况下，现有的缺失数据方法可能不足以对这些观察到的自我启动报告进行建模。在某些 EMA 研究中，与自我启动报告不同的随机提示可能会转换为事件报告。例如，如果在随机提示期间评估了受试者关于事件（例如吸烟）的情况，并且确定在提示时受试者正在从事该事件，则可能会发生这种转换。这种转换后的提示可以提供有关受试者事件报告无反应性的一些信息。此外，这种无反应性可能与主要的纵向 EMA 结果（例如情绪）相关联，在这种情况下，可以对无反应性和情绪结果进行联合建模。在这里，我们提出了一种共享参数位置-尺度模型，通过共享随机效应将情绪的主要结果模型与受试者无反应性的模型联系起来，这些随机效应可以描述受试者的情绪水平、情绪变化模式和情绪可变性。通过模拟和真实数据分析，我们提出的模型被证明更具信息性，对参数的覆盖更好，并且比更传统的模型更能拟合数据。
