Gomon Daniel, Putter Hein, Fiocco Marta, Signorelli Mirko
Mathematical Institute, Leiden University, Leiden, the Netherlands.
Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, the Netherlands.
Stat Methods Med Res. 2024 Feb;33(2):256-272. doi: 10.1177/09622802231224631. Epub 2024 Jan 9.
Dynamically predicting patient survival probabilities using longitudinal measurements has become of great importance with routine data collection becoming more common. Many existing models utilize a multi-step landmarking approach for this problem, mostly due to its ease of use and versatility but unfortunately most fail to do so appropriately. In this article we make use of multivariate functional principal component analysis to summarize the available longitudinal information, and employ a Cox proportional hazards model for prediction. Additionally, we consider a centred functional principal component analysis procedure in an attempt to remove the natural variation incurred by the difference in age of the considered subjects. We formalize the difference between a 'relaxed' landmarking approach where only validation data is landmarked and a 'strict' landmarking approach where both the training and validation data are landmarked. We show that a relaxed landmarking approach fails to effectively use the information contained in the longitudinal outcomes, thereby producing substantially worse prediction accuracy than a strict landmarking approach.
随着常规数据收集变得越来越普遍，利用纵向测量动态预测患者生存概率变得极为重要。许多现有模型针对此问题采用多步地标法，主要是因为其易用性和通用性，但不幸的是，大多数模型并未正确使用该方法。在本文中，我们利用多元函数主成分分析来总结可用的纵向信息，并采用Cox比例风险模型进行预测。此外，我们考虑了一种中心化函数主成分分析程序，试图消除所考虑对象年龄差异带来的自然变异。我们明确了“宽松”地标法（仅对标定验证数据）和“严格”地标法（对标定训练数据和验证数据）之间的差异。我们表明，宽松地标法无法有效利用纵向结果中包含的信息，因此其预测准确性比严格地标法差得多。
