Missing data in non-stationary multivariate time series from digital studies in Psychiatry
精神医学数字研究中非平稳多元时间序列的缺失数据AI 解读 ↓
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精神医学数字研究中非平稳多元时间序列的缺失数据
- 研究问题
- 移动设备收集的结果、暴露与协变量相互依赖且非平稳时，如何处理缺失数据，避免时间依赖和历史信息使推断更加困难？
- 方法与思路
- 作者提出状态空间模型的蒙特卡洛 EM 算法，比较不同缺失机制下的平稳与非平稳模拟，并分析多年智能手机观察数据。
- 材料陈述的贡献
- 摘要称该方法在模拟中优于若干常用插补策略，并应用于双相障碍和精神分裂症患者，研究数字社会连接与负面情绪的关联。
- 阅读关注点
- 应核对状态空间设定、非平稳性的处理、缺失机制与算法收敛，以及模拟比较的公平性和实际应用中关联分析的解释边界。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未提供模型细节、缺失假设或优势数值，观察性应用也未明确建立因果识别。
原文摘要
Mobile technology (e.g., mobile phones and wearable devices) provides scalable methods for collecting physiological and behavioral biomarkers in patients' naturalistic settings, as well as opportunities for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous data collection through mobile devices generates highly complex data: entangled multivariate time series of outcomes, exposures, and covariates. Missing data is a pervasive problem in biomedical and social science research, and Ecological Momentary Assessment (EMA) data in psychiatric research is no exception. However, the complex data structure of multivariate time series and their non-stationary nature make missing data a major challenge for proper inference. Additional historical information included in time series analyses exacerbates the issue of missing data and also introduces problems for confounding adjustment. The majority of existing imputation methods are either designed for stationary time series or for longitudinal data with limited follow-up periods. The limited work on non-stationary time series either focuses on missing exogenous information or ignores the complex temporal dependence among outcomes, exposures, and covariates. We propose a Monte Carlo Expectation Maximization algorithm for the state space model (MCEM-SSM) to effectively handle missing data in non-stationary entangled multivariate time series. We demonstrate the method's advantages over other widely used missing data imputation strategies through simulations of both stationary and non-stationary time series, subject to various missing mechanisms. Finally, we apply the MCEM-SSM to a multi-year smartphone observational study of bipolar and schizophrenia patients to investigate the association between digital social connectivity and negative mood.