Causal estimands and identification of time-varying effects in non-stationary time series from N-of-1 mobile device data
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单人移动设备非平稳时间序列中的时变因果效应：目标量与识别
- 研究问题
- 如何在单人移动健康数据中定义和识别随时间变化的短期及长期治疗效应，同时处理暴露、结局和协变量之间的反馈？
- 方法与思路
- 作者提出时变因果目标量及识别假设，在状态空间模型中实现 g-formula 估计，并提出模拟推荐策略和正值性诊断图。
- 材料陈述的贡献
- 摘要称方法容纳非平稳效应与反馈，在双相障碍患者手机数据中发现跨时间和个体异质性，并支持个性化短期推荐。
- 阅读关注点
- 应核对短期与长期目标量的干预定义、反馈下的识别假设、状态空间设定，以及正值性诊断与推荐策略的适用范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列明识别假设，诊断图及观察数据结果不足以单独验证因果识别。
原文摘要
Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures and covariates, presented as intensive longitudinal or multivariate time series data. The high frequency of measurements enables granular and dynamic evaluation of treatment effect, revealing their persistence and accumulation over time. Existing methods predominantly focus on the contemporaneous effect, temporal-average, or population-average effects, assuming stationarity or invariance of treatment effects over time, which are inadequate both conceptually and statistically to capture dynamic treatment effects in personalized mobile health data. We here propose new causal estimands for multivariate time series in N-of-1 studies. These estimands summarize how time-varying exposures impact outcomes in both short- and long-term. We propose identifiability assumptions and a g-formula estimator that accounts for exposure-outcome and outcome-covariate feedback. The g-formula employs a state space model framework innovatively to accommodate time-varying behavior of treatment effects in non-stationary time series. We apply the proposed method to a multi-year smartphone observational study of bipolar patients and estimate the dynamic effect of phone-based communication on mood of patients with bipolar disorder in an N-of-1 setting. Our approach reveals substantial heterogeneity in treatment effects over time and across individuals. A simulation-based strategy is also proposed for the development of a short-term, dynamic, and personalized treatment recommendation based on patient's past information, in combination with a novel positivity diagnostics plot, validating proper causal inference in time series data.