Deep Neural Networks for Semiparametric Frailty Models via H-likelihood
Hangbin Lee IL DO HA Youngjo Lee
For prediction of clustered time-to-event data, we propose a new deep neural
network based gamma frailty model (DNN-FM). An advantage of the proposed model
is that the joint maximization of the new h-likelihood provides maximum
likelihood estimators for fixed parameters and best unbiased predictors for
random frailties. Thus, the proposed DNN-FM is trained by using a negative
profiled h-likelihood as a loss function, constructed by profiling out the
non-parametric baseline hazard. Experimental studies show that the proposed
method enhances the prediction performance of the existing methods. A real data
analysis shows that the inclusion of subject-specific frailties helps to
improve prediction of the DNN based Cox model (DNN-Cox).