Borrowing strength between unaligned binary time-series via Bayesian nonparametric rescaling of Unified Skewed Normal priors
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通过统一偏斜正态先验的贝叶斯非参数重缩放共享未对齐二元时间序列信息
- 研究问题
- 不同患者的二元纵向记录没有时间对齐，却可能出现相似的发作聚集模式时，如何共享信息并保留个体和时间异质性？
- 方法与思路
- 作者结合近乎个体专属的状态空间模型与模式概率上的 Pitman–Yor 先验，并利用统一偏斜正态族构造后验抽样策略。
- 材料陈述的贡献
- 摘要提出通过临床关注模式的概率实现跨患者信息共享，从而绕开潜在状态轨迹未对齐给直接层级建模带来的困难。
- 阅读关注点
- 应核对聚集模式及分组概率如何定义、先验重缩放如何连接各轨迹，以及后验计算、信息共享程度与个体差异的权衡。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收状态未核实。摘要未给出具体先验设定、收敛诊断和实证效果，跨场景适用性仍需核对。
原文摘要
We define a Bayesian semi-parametric model to effectively conduct inference with unaligned longitudinal binary data. The proposed strategy is motivated by data from the Human Epilepsy Project (HEP), which collects seizure occurrence data for epilepsy patients, together with relevant covariates. The model is designed to flexibly accommodate the particular challenges that arise with such data. First, epilepsy data require models that can allow for extensive heterogeneity, across both patients and time. With this regard, state space models offer a flexible, yet still analytically amenable class of models. Nevertheless, seizure time-series might share similar behavioral patterns, such as local prolonged periods of elevated seizure presence, which we refer to as "clumping". Such similarities can be used to share strength across patients and define subgroups. However, due to the lack of alignment, straightforward hierarchical modeling of latent state space parameters is not practicable. To overcome this constraint, we construct a strategy that preserves the flexibility of individual trajectories while also exploiting similarities across individuals to borrow information through a nonparametric prior. On the one hand, heterogeneity is ensured by (almost) subject-specific state-space submodels. On the other, borrowing of information is obtained by introducing a Pitman-Yor prior on group-specific probabilities for patterns of clinical interest. We design a posterior sampling strategy that leverages recent developments of binary state space models using the Unified Skewed Normal family (SUN). The model, which allows the sharing of information across individuals with similar disease traits over time, can more generally be adapted to any setting characterized by unaligned binary longitudinal data.