论文标题
型号双win随机化（MOTR）用于估计自己的重复治疗效果
Model-Twin Randomization (MoTR) for Estimating One's Own Recurring Individual Treatment Effect
论文作者
论文摘要
由于移动应用程序和可穿戴传感器，时间密集的单人“小数据”已广泛使用。许多护理人员和自我追踪者都希望使用这些数据来帮助特定的人改变其行为以实现所需的健康成果。理想情况下，这涉及使用该人自己的观察时间序列数据来辨别可能的原因。在本文中，我们估计体育活动对睡眠持续时间的个体内平均治疗效果，反之亦然。我们介绍了模型双随机化（MOTR；“ Motor”）方法，用于分析个人的密集纵向数据。正式地，MOTR是在串行干扰下G型（即标准化，后门调整）的应用。它估计了稳定的重复效应，就像在N-1-1试验和单个案例实验设计中所做的那样。我们将我们的标准方法方法（可能有可能的混淆）进行比较，以展示如何使用因果推理来对健康行为改变更好的个性化建议，并分析作者自己的Fitbit步骤和睡眠数据多达近八年的时间。
Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration, and vice-versa. We introduce the model twin randomization (MoTR; "motor") method for analyzing an individual's intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze up to almost eight years of the authors' own Fitbit steps and sleep data.
