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In different areas of research, multiple recurrent competing risks (RCR) areoften observed on the same observational unit. For instance, different types ofcancer relapses are observed on the same patient and several types of componentfailures are observed in the same reliability system. When a terminal event(TE) such as death is also observed on the same unit, since the RCRs aregenerally informative about death, we develop joint dynamic models thatsimultaneously model the RCRs and the TE. A key interest of such joint dynamicmodeling is to predict time-to-terminal event (TTTE) for new units that havenot experienced the TE by the end of monitoring period. In this paper, wepropose a simulation approach to predict TTTE which arises from a class ofjoint dynamic models of RCRs and TE. The proposed approach can be applied toproblems in precision medicine and potentially many other settings. Thesimulation method makes personalized predictions of TTTE and provides anempirical predictive distribution of TTTE. Predictions of the RCR occurrencesbeyond a possibly random monitoring time and leading up to the TE occurrenceare also produced. The approach is dynamic in that each simulated occurrence ofRCR increases the amount of knowledge we obtain on an observational unit whichinforms the simulation of TTTE. We demonstrate the approach on a syntheticdataset and evaluate predictive accuracy of the prediction method through5-fold cross-validation using empirical Brier score.

Prediction of Time-to-terminal Event (TTTE) in a Class of Joint Dynamic Models