潜在矩模型用于复发二元结果：一种贝叶斯和拟分布方法
Latent Moment Models for Recurrent Binary Outcomes: A Bayesian and Quasi-Distributional Approach
AI总结：
本文提出两种模型，通过建模潜在矩来捕捉复发二元结果中的时间变化风险，提升医疗数据的可解释性和鲁棒性。
摘要：
个体内部的复发二元结果，如医院再入院，往往反映随时间演变的潜在风险过程。传统方法如广义线性混合模型和广义估计方程估计平均风险，但无法捕捉时间变化中的变异性、不对称性和尾部行为。我们引入了两种统计框架，将每个二元事件视为从由位置、尺度、偏度和峰度定义的时间变化潜在分布中抽取的阈值值的结果。而不是将这四个量视为非参数矩估计量，我们将它们建模为在灵活的潜在分布族中可解释的潜在矩。第一个，BLaS-Recurrent，是一种使用sinh-arcsinh分布（一种提供对不对称性和尾部重量显式控制的参数族）的贝叶斯模型，用于估计潜在矩轨迹；第二个，QuaD-Recurrent，是一种拟分布方法，通过灵活的非参数表面将模拟的矩向量映射到事件概率。两种模型均支持时间依赖的协变量、序列相关性和多重成员结构。模拟研究显示，这些模型在校准、可解释性和稳健性方面优于标准模型。应用于MIMIC-IV数据库中的ICU再入院数据，两种方法揭示了传统方法所遗漏的潜在风险中的临床有意义的模式，如右偏的升级和分散的扩大。这些模型为医疗领域的纵向二元结果提供了可解释、分布敏感的工具，同时明确指出潜在的“矩”总结但不唯一确定底层分布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
