[论文解读] Latent Moment Models for Recurrent Binary Outcomes: A Bayesian and Quasi-Distributional Approach

Niloofar Ramezani, Lori P. Selby+2 more|arXiv (Cornell University)|Feb 22, 2026

Sepsis Diagnosis and Treatment被引用 0

一句话总结

论文引入了两种框架（Bayesian BLaS-Recurrent 和准分布 QuaD-Recurrent）通过时变潜在矩来建模重复的二元结果，在校准和可解释性方面优于标准方法。

ABSTRACT

Recurrent binary outcomes within individuals, such as hospital readmissions, often reflect latent risk processes that evolve over time. Conventional methods like generalized linear mixed models and generalized estimating equations estimate average risk but fail to capture temporal changes in variability, asymmetry, and tail behavior. We introduce two statistical frameworks that model each binary event as the outcome of a thresholded value drawn from a time-varying latent distribution defined by its location, scale, skewness, and kurtosis. Rather than treating these four quantities as nonparametric moment estimators, we model them as interpretable latent moments within a flexible latent distributional family. The first, BLaS-Recurrent, is a Bayesian model using the sinh-arcsinh distribution (a parametric family that provides explicit control over asymmetry and tail weight) to estimate latent moment trajectories; the second, QuaD-Recurrent, is a quasi-distributional approach that maps simulated moment vectors to event probabilities using a flexible nonparametric surface. Both models support time-dependent covariates, serial correlation, and multiple membership structures. Simulation studies show improved calibration, interpretability, and robustness over standard models. Applied to ICU readmission data from the MIMIC-IV database, both approaches uncover clinically meaningful patterns in latent risk, such as right-skewed escalation and widening dispersion, that are missed by traditional methods. These models provide interpretable, distribution-sensitive tools for longitudinal binary outcomes in healthcare while explicitly acknowledging that latent "moments" summarize but do not uniquely determine the underlying distribution.

研究动机与目标

• 需要建模重复二元结果在变异性、偏态和尾部行为上的时间变化动机
• 提出潜在矩框架，其中位置、尺度、偏度和峰度随时间演化
• 开发两种方法：一个使用 sinh-arcsinh 潜在分布的贝叶斯模型，以及一个将矩映射到概率的准分布映射
• 允许时间依赖协变量、序列相关性和多成员结构以捕捉潜在风险轨迹

提出的方法

• 将每个二元事件建模为来自随时间变化潜在分布的阈值抽样，潜在矩包括位置、尺度、偏度、峰度
• 使用 BLaS-Recurrent（sinh-arcsinh 分布的贝叶斯方法）来估计潜在矩轨迹
• 引入 QuaD-Recurrent，一种准分布方法，通过灵活的非参数曲面将模拟的矩向量映射到事件概率
• 在这两种框架中容纳时间依赖协变量、序列相关和多成员结构
• 通过仿真演示相较于标准模型在校准、解释性和鲁棒性上的改进

实验结果

研究问题

• RQ1 潜在矩（位置、尺度、偏度、峰度）是否能够捕捉重复二元结果的变异性和尾部行为的时间变化？
• RQ2 贝叶斯（BLaS-Recurrent）与准分布（QuaD-Recurrent）方法是否在对纵向二元数据的校准和可解释性方面优于传统 GLMMs 或 GEE？
• RQ3 这些模型在实践中如何处理时间依赖协变量、序列相关和多成员结构？
• RQ4 在实际医疗数据中使用这些模型会出现何种潜在风险模式（如右偏上升、离散度增大）？

主要发现

• 仿真研究显示在校准、可解释性和鲁棒性方面优于标准模型
• 应用于来自 MIMIC-IV 数据库的 ICU 重新住院数据，两种方法均揭示潜在风险的临床有意义模式
• 两种模型都揭示出传统方法错过的潜在风险的右偏上升和离散度扩张
• 这些框架为医疗保健中纵向二元结果提供可解释、对分布敏感的工具
• 他们也承认潜在矩可以总结信息但并不唯一地决定潜在分布的底层形态