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.

翻译：移动技术使得对个体行为、社交互动、症状及其他健康状况进行前所未有的持续监测成为可能，为精神疾病病因学的治疗进展和科学发现提供了巨大机遇。持续收集移动数据会产生一种新型数据：结局变量、暴露变量和协变量相互交织的多元时间序列。缺失数据是生物医学和社会科学研究中普遍存在的问题，而在精神病学研究中利用移动设备进行生态瞬时评估（EMA）亦不例外。然而，多元时间序列的复杂结构为正确处理缺失数据以进行因果推断带来了新挑战。数据插补通常被推荐用于提升数据利用率和估计效率。现有的大多数插补方法要么针对随访时间有限的纵向数据设计，要么适用于平稳时间序列，与潜在非平稳时间序列不兼容。在精神病学领域，由于症状和治疗方案可能随时间发生剧烈变化，非平稳数据十分常见。为处理可能存在的非平稳多元时间序列中的缺失数据，我们提出了一种基于状态空间模型的新型多重插补策略（SSMmp）及其计算效率更高的变体（SSMimpute）。通过评估其在平稳与非平稳时间序列模拟中（考虑多种缺失机制）的理论性质和实证表现，我们证明了这些方法相较于其他广泛使用的缺失数据处理策略具有优势。我们应用SSMimpute方法，利用一项针对双相情感障碍患者的多年观察性智能手机研究数据，在控制混杂变量的情况下，探究了社交网络规模与负面情绪之间的关联。