In recent years, the growing availability of biomedical datasets featuring numerous longitudinal covariates has motivated the development of several multi-step methods for the dynamic prediction of survival outcomes. These methods employ either mixed-effects models or multivariate functional principal component analysis to model and summarize the longitudinal covariates' evolution over time. Then, they use Cox models or random survival forests to predict survival probabilities, using as covariates both baseline variables and the summaries of the longitudinal variables obtained in the previous modelling step. Because these multi-step methods are still quite new, to date little is known about their applicability, limitations, and predictive performance when applied to real-world data. To gain a better understanding of these aspects, we performed a benchmarking of these multi-step methods (and two simpler prediction approaches) using three datasets that differ in sample size, number of longitudinal covariates and length of follow-up. We discuss the different modelling choices made by these methods, and some adjustments that one may need to do in order to be able to apply them to real-world data. Furthermore, we compare their predictive performance using multiple performance measures and landmark times, assess their computing time, and discuss their strengths and limitations.

翻译：近年来，随着具有大量纵向协变量的生物医学数据集的日益普及，推动了多种用于生存结局动态预测的多步方法的发展。这些方法采用混合效应模型或多变量函数主成分分析来建模和总结纵向协变量随时间的变化。随后，它们使用Cox模型或随机生存森林来预测生存概率，并将基线变量以及在前一步建模中获得的纵向变量摘要作为协变量。由于这些多步方法仍然相对较新，迄今为止，关于它们在应用于实际数据时的适用性、局限性和预测性能知之甚少。为了更好地理解这些方面，我们使用三个在样本量、纵向协变量数量和随访时间长度上不同的数据集，对这些多步方法（以及两种更简单的预测方法）进行了基准测试。我们讨论了这些方法所采用的不同建模选择，以及为了能够将其应用于实际数据可能需要进行的调整。此外，我们使用多种性能指标和地标时间比较了它们的预测性能，评估了它们的计算时间，并讨论了它们的优势和局限性。