面向非平稳多元时间序列缺失数据的状态空间模型多重插补及其在数字精神病学中的应用
State space model multiple imputation for missing data in non-stationary multivariate time series with application in digital Psychiatry
AI总结：
针对非平稳多元时间序列缺失数据问题，提出基于状态空间模型的多重插补方法SSMmp及其高效变体SSMimpute，通过理论与模拟验证其优势，并应用于双相情感障碍患者智能手机数据中社交网络规模与负面情绪关联的研究。
摘要：
移动技术使对个体行为、社会互动、症状及其他健康状况进行前所未有的连续监测成为可能，为精神病学病因相关的治疗进展和科学发现提供了巨大机遇。移动数据的连续采集催生了一种新型数据：由结局、暴露和协变量构成的纠缠式多元时间序列。缺失数据是生物医学和社会科学研究中普遍存在的问题，在精神病学研究中利用移动设备进行的生态瞬时评估（EMA）也不例外。然而，多元时间序列的复杂结构为在正确因果推断下处理缺失数据带来了新的挑战。通常推荐采用数据插补来提高数据效用和估计效率。现有的大多数插补方法要么针对随访次数有限的纵向数据设计，要么针对平稳时间序列设计，这与潜在的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。为解决可能非平稳的多元时间序列中的缺失数据问题，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）。我们通过评估其理论性质以及在多种缺失机制下的平稳和非平稳时间序列模拟中的经验表现，展示了它们相对于其他广泛使用的缺失数据处理策略的优势。我们将SSMimpute应用于一项为期多年的双相情感障碍患者观察性智能手机研究，在控制混杂变量的条件下，考察社交网络规模与负面情绪之间的关联。
英文摘要：
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.
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