Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some point in time. Determining the transition point of the disease trajectory to a more severe state is important clinically for disease management and for avoiding irreversible vision loss. Based on this, we present a unified statistical modeling framework that permits prediction of the timing and spatial location of future vision loss and informs clinical decisions regarding disease progression. The developed method incorporates anatomical information to create a biologically plausible data-generating model. We accomplish this by introducing a spatially varying coefficients model that includes spatially varying change points to detect structural shifts in both the mean and variance process of VF data across both space and time. The VF location-specific change point represents the underlying, and potentially censored, timing of true change in disease trajectory while a multivariate spatial boundary detection structure is introduced that accounts for the complex spatial connectivity of the VF and optic disc. We show that our method improves estimation and prediction of multiple aspects of disease management in comparison to existing methods through simulation and real data application. The R package spCP implements the new methodology.

通过视野 (VF) 数据测量的青光眼疾病进展通常定义为一段相对稳定的时期，随后在某个时间点视觉能力突然下降。确定疾病轨迹向更严重状态的转变点对于疾病管理和避免不可逆的视力丧失具有重要的临床意义。在此基础上，我们提出了一个统一的统计模型框架，可以预测未来视力丧失的时间和空间位置，并为有关疾病进展的临床决策提供信息。开发的方法结合了解剖信息来创建生物学上合理的数据生成模型。我们通过引入空间变化的系数模型来实现这一目标，该模型包括空间变化的变化点，以检测 VF 数据在空间和时间上的均值和方差过程中的结构变化。 VF 位置特定的变化点代表疾病轨迹真实变化的潜在且可能被审查的时间，同时引入了多元空间边界检测结构来解释 VF 和视盘的复杂空间连接性。我们通过模拟和实际数据应用表明，与现有方法相比，我们的方法改进了疾病管理多个方面的估计和预测。 R 包spCP实现了新方法。