State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital Psychiatry
非平稳多变量时间序列的状态空间多重填补及数字精神医学应用AI 解读 ↓
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非平稳多变量时间序列的状态空间多重填补及数字精神医学应用
- 研究问题
- 面对手机连续监测产生的非平稳多变量时间序列，如何填补缺失数据，同时保留结局、暴露与协变量之间的动态关系？
- 方法与思路
- 作者提出状态空间模型多重填补方法 SSMmp 及计算更高效的 SSMimpute，在不同平稳性和缺失机制下进行理论与模拟评估。
- 材料陈述的贡献
- 摘要称，两种方法在理论性质和模拟表现上展示优势，并将 SSMimpute 用于控制混杂后研究社交网络规模与负面情绪的关联。
- 阅读关注点
- 应核对状态空间结构、非平稳性的表示及缺失机制假设，并检查填补不确定性的传递，以及应用中的关联解释与因果边界。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及接收未核实；摘要未列出理论保证及改善幅度，控制混杂的关联分析不自动确立因果。
原文摘要
Mobile technology enables unprecedented continuous monitoring of an individual's behavior, social interactions, symptoms, and other health conditions, presenting an enormous opportunity for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous collection of mobile data results in the generation of a new type of data: entangled multivariate time series of outcome, exposure, and covariates. Missing data is a pervasive problem in biomedical and social science research, and the Ecological Momentary Assessment (EMA) using mobile devices in psychiatric research is no exception. However, the complex structure of multivariate time series introduces new challenges in handling missing data for proper causal inference. Data imputation is commonly recommended to enhance data utility and estimation efficiency. The majority of available imputation methods are either designed for longitudinal data with limited follow-up times or for stationary time series, which are incompatible with potentially non-stationary time series. In the field of psychiatry, non-stationary data are frequently encountered as symptoms and treatment regimens may experience dramatic changes over time. To address missing data in possibly non-stationary multivariate time series, we propose a novel multiple imputation strategy based on the state space model (SSMmp) and a more computationally efficient variant (SSMimpute). We demonstrate their advantages over other widely used missing data strategies by evaluating their theoretical properties and empirical performance in simulations of both stationary and non-stationary time series, subject to various missing mechanisms. We apply the SSMimpute to investigate the association between social network size and negative mood using a multi-year observational smartphone study of bipolar patients, controlling for confounding variables.