标题： 空间参考的多状态现状数据的单调单指标模型 显示英文标题

标题： A monotone single index model for spatially-referenced multistate current status data

摘要： 多状态疾病进展的评估在生物医学研究中很常见，例如在牙周病（PD）中。 然而，多状态现况终点的存在，即在已知起始状态之后的随机检查时间点，只能获得每个受试者疾病状态进展的单一快照，这使得推断框架变得复杂。 此外，这些终点可能是聚类的，并且具有空间相关性，即同一受试者内邻近的牙齿组可能经历相似的PD状态，而远离的牙齿则不同。 受一项记录PD进展的临床研究的启发，我们提出了一种贝叶斯半参数加速失效时间模型，采用逆Wishart提议来适应（空间）随机效应，并且误差项灵活地服从高斯分布的狄利克雷过程混合。 为了临床可解释性，事件时间的系统成分使用单调单指标模型进行建模，其中（未知）链接函数通过一种新颖的集成基展开方法进行估计，基系数赋予了约束高斯过程先验。 除了建立参数可识别性外，我们通过椭圆切片抽样、快速循环嵌入技术和硬约束平滑相结合的方法实现了可扩展计算，从而实现了参数以及状态占用和转移概率的简单估计。 通过合成数据，我们研究了我们的贝叶斯估计的有限样本性质及其在模型误设下的表现。 我们还通过应用到真实的临床PD数据集来说明我们的方法。 显示英文摘要

摘要： Assessment of multistate disease progression is commonplace in biomedical research, such as, in periodontal disease (PD). However, the presence of multistate current status endpoints, where only a single snapshot of each subject's progression through disease states is available at a random inspection time after a known starting state, complicates the inferential framework. In addition, these endpoints can be clustered, and spatially associated, where a group of proximally located teeth (within subjects) may experience similar PD status, compared to those distally located. Motivated by a clinical study recording PD progression, we propose a Bayesian semiparametric accelerated failure time model with an inverse-Wishart proposal for accommodating (spatial) random effects, and flexible errors that follow a Dirichlet process mixture of Gaussians. For clinical interpretability, the systematic component of the event times is modeled using a monotone single index model, with the (unknown) link function estimated via a novel integrated basis expansion and basis coefficients endowed with constrained Gaussian process priors. In addition to establishing parameter identifiability, we present scalable computing via a combination of elliptical slice sampling, fast circulant embedding techniques, and smoothing of hard constraints, leading to straightforward estimation of parameters, and state occupation and transition probabilities. Using synthetic data, we study the finite sample properties of our Bayesian estimates, and their performance under model misspecification. We also illustrate our method via application to the real clinical PD dataset.