Statistical issues in survival analysis (dynamic survival)
Figure 1. tdAUC and brier score results obtained by predicting the true survival probabilities for each simulation scenario.
June 18, 2025 The goal of this paper was to bridge gaps in understanding how to use machine learning (ML) methods for survival by presenting a comprehensive study comparing various ML methods for dynamic survival analysis. They sought to provide researchers and practitioners in the field of cognitive health, and healthcare in general, with a clearer understanding of the strengths and weaknesses of these methods and the nuances of their implementation. They said their main contributions of this study were (i) the comparison of methods for dynamic survival analysis across diverse scenarios, including both statistical approaches, such as MFPCA, and neural network-based methods, (ii) the separate investigation of the contributions to the system performance of longitudinal and survival models by testing multiple combinations and (iii) the comparison of several landmarking strategies that can be used during model training. For dynamic prediction, they discussed using landmarking but building separate survival models at each landmark time. Joint modeling of longitudinal and survival data also exist. They then came up with a two-stage modeling approach, by which they can effectively combine different longitudinal and survival models. In the first stage, a longitudinal model is used and in the second stage, predictions of the longitudinal model are incorporated into the survival model. They felt this strategy works well to avoid joint modeling complexities. They discussed several landmark approaches: none, strict, super, and random. None uses all data to make decisions. The strict uses a separate model trained at each landmark time but risks losing data is not used in each landmarked dataset. The super also does what the strict does but ends up