SurvSurf: a partially monotonic neural network for first-hitting time prediction of intermittently observed discrete and continuous sequential events
This work addresses the need for accurate and theoretically sound survival prediction models in fields like healthcare or reliability analysis, though it is incremental as it builds on existing neural network and survival analysis methods.
The authors tackled the problem of predicting first-hitting times for sequential events with a neural network model called SurvSurf, which ensures monotonicity in cumulative incidence functions and handles discrete/continuous data, showing superiority over existing models in simulations and real-world datasets with metrics like MSE and IBS.
We propose a neural-network based survival model (SurvSurf) specifically designed for direct and simultaneous probabilistic prediction of the first hitting time of sequential events from baseline. Unlike existing models, SurvSurf is theoretically guaranteed to never violate the monotonic relationship between the cumulative incidence functions of sequential events, while allowing nonlinear influence from predictors. It also incorporates implicit truths for unobserved intermediate events in model fitting, and supports both discrete and continuous time and events. We also identified a variant of the Integrated Brier Score (IBS) that showed robust correlation with the mean squared error (MSE) between the true and predicted probabilities by accounting for implied truths about the missing intermediate events. We demonstrated the superiority of SurvSurf compared to modern and traditional predictive survival models in two simulated datasets and two real-world datasets, using MSE, the more robust IBS and by measuring the extent of monotonicity violation.