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Abstract：
Monitoring disease progression often involves tracking biomarker measurements over time. Joint models (JMs) for longitudinal and survival data provide a framework to explore the relationship between time-varying biomarkers and patients\' event outcomes, offering the potential for personalized survival predictions. In this article, we introduce the linear state space dynamic survival model for handling longitudinal and survival data. This model enhances the traditional linear Gaussian state space model by including survival data. It differs from the conventional JMs by offering an alternative interpretation via differential or difference equations, eliminating the need for creating a design matrix. To showcase the model\'s effectiveness, we conduct a simulation case study, emphasizing its performance under conditions of limited observed measurements. We also apply the proposed model to a dataset of pulmonary arterial hypertension patients, demonstrating its potential for enhanced survival predictions when compared with conventional risk scores.
摘要：
监测疾病进展通常涉及随时间跟踪生物标志物测量。纵向和生存数据的联合模型（JMs）提供了一个框架来探索时变生物标志物与患者事件结果之间的关系。提供个性化生存预测的潜力。在这篇文章中,引入线性状态空间动态生存模型来处理纵向和生存数据。该模型通过包含生存数据来增强传统的线性高斯状态空间模型。它与传统的JMs不同，通过微分方程或差分方程提供了另一种解释，消除了创建设计矩阵的需要。为了展示模型的有效性，我们进行了一个模拟案例研究，强调其在有限的观察测量条件下的性能。我们还将提出的模型应用于肺动脉高压患者的数据集，与传统风险评分相比，证明其增强生存预测的潜力。
