Dynamic prediction of time to event with survival curves
This work addresses the need for precise individual-level predictions in proactive patient management, though it appears incremental by enhancing existing models with explanatory knowledge.
The paper tackles the problem of dynamic prediction of time-to-event outcomes in healthcare by integrating explanatory survival analysis with data-adaptive models, resulting in reliable prediction of patient failure times using individual survival curves.
With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource. One key enabler is the dynamic prediction of time-to-event outcomes. Conventional explanatory statistical approach lacks the capability of making precise individual level prediction, while the data adaptive binary predictors does not provide nominal survival curves for biologically plausible survival analysis. The purpose of this article is to elucidate that the knowledge of explanatory survival analysis can significantly enhance the current black-box data adaptive prediction models. We apply our recently developed counterfactual dynamic survival model (CDSM) to static and longitudinal observational data and testify that the inflection point of its estimated individual survival curves provides reliable prediction of the patient failure time.