面向多个纵向标记物与竞争事件的联合时空模型
A joint spatiotemporal model for multiple longitudinal markers and competing events
AI总结：
针对慢性疾病无明确发病时间难以定义联合模型参考时间的问题，提出原因特异性联合时空模型，可捕捉潜在疾病年龄与纵向结局排序的影响，在ALS数据上性能与传统模型相当且参数更少。
摘要：
非终末事件可代表患者生命中具有重要意义的变化，因此更好地理解和预测其发生能为个体提供有价值的信息。在纵向标记物可为这类事件提供参考信息的场景下，已有研究开发了带竞争风险的联合模型，这类模型的精度依赖于参考时间，通常选用疾病发病时间作为参考。然而，慢性疾病没有明确的发病时间，导致难以定义精确的参考时间。我们提出了一种原因特异性联合时空（Joint cause-specific Spatiotemporal）模型，以克服这一局限，同时捕捉共享潜在过程——即与纵向结局排序（空间维度）相关的潜在年龄（时间维度）。首先，我们在类真实模拟数据上验证了该模型；随后，我们使用PRO-ACT数据集，在真实肌萎缩侧索硬化（ALS）数据上将该模型与共享随机效应联合模型进行基准对比；最后，为展示该模型如何用于描述性任务，我们分析了性别和发病部位对ALS进展以及无创通气启动时间的影响。原因特异性联合时空模型取得了与共享随机效应联合模型相近的性能，同时以更少的参数捕捉了潜在疾病年龄，以及纵向结局排序对事件发生的影响。应用研究验证了纵向结局的已有研究结果，并展示了该模型的解读方式。所提出的方法通过拆解疾病的时间维度与空间维度，为在未来临床试验中捕捉有意义的疾病变化开辟了新方向。
英文摘要：
Non-terminal events can represent a meaningful change in a patient's life. Thus, better understanding and predicting their occurrence can bring valuable information to individuals. In a context where longitudinal markers could inform these events, joint models with competing risks have been developed. Their precision relies on a reference time for which disease onset is often used. Nevertheless, chronic diseases have no clear onset, making it difficult to define a precise reference time. We propose a Joint cause-specific Spatiotemporal model to overcome this limitation and to capture a shared latent process, a latent age (temporal aspect), associated with the ordering of the longitudinal outcomes (spatial aspect). First, we validated our model on simulated real-like data. Then, we benchmarked our model with a shared-random-effect joint model on real ALS data using the PRO-ACT dataset. Finally, to show how the model could be used for description tasks, we analysed the impact of sex and onset site on the progression of ALS as well as the initiation of Non-Invasive Ventilation. The Joint cause-specific spatiotemporal model achieved similar performance to the shared random effect joint model while capturing the latent disease age and the impact of the ordering of longitudinal outcomes on the occurrence of the events with fewer parameters. The application study confirmed existing results for the Longitudinal outcomes and showed how to interpret the model. The proposed approach by disentangling a temporal and a spatial aspect of the disease opens the perspective to capture meaningful change in future clinical trials.
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。
