移动健康研究中病例与对照患者的个性化用药反应和分类分析：mPower病例研究
On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study
摘要：
在这项工作中，我们提供了一对夫妇的贡献，在移动健康应用程序中的智能手机收集的纵向数据的分析。首先，我们提出了一种新的统计方法来解开观察性研究中的个性化治疗和“一天中的时间”效应。在没有未测量的混杂因素的假设下，我们展示了如何使用条件独立关系的数据，以确定是否在活动任务之间的差异进行之前和之后的参与者已经服用药物，可能是由于药物的影响或“时间的一天”的效果（或仍然两者）。其次，我们表明，从一个给定的研究参与者收集的智能手机数据可以代表一个“数字指纹”的参与者，和分类的情况下/控制标签，使用纵向数据构建，可以显示人为改善性能时，每个参与者的数据都包括在训练和测试集。我们使用mPower研究前6个月收集的数据来说明我们的贡献。
英文摘要：
In this work we provide a couple of contributions to the analysis of longitudinal data collected by smartphones in mobile health applications. First, we propose a novel statistical approach to disentangle personalized treatment and "time-of-the-day" effects in observational studies. Under the assumption of no unmeasured confounders, we show how to use conditional independence relations in the data in order to determine if a difference in performance between activity tasks performed before and after the participant has taken medication, are potentially due to an effect of the medication or to a "time-of-the-day" effect (or still to both). Second, we show that smartphone data collected from a given study participant can represent a "digital fingerprint" of the participant, and that classifiers of case/control labels, constructed using longitudinal data, can show artificially improved performance when data from each participant is included in both training and test sets. We illustrate our contributions using data collected during the first 6 months of the mPower study.
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