Table 3.

Simulation results for Section 5.2 ( covariates, and censoring) presented in integrated squared survival error (ISSE) over the observed support

		Models	Log-Normal	Weibull	Gamma
Linear effects	Single models	Cox-Lasso	2.43	1.68	2.50
		Cox-boosting	2.60	1.75	2.66
		RSF	2.46	1.86	2.50
	Flexible models	Stacking	2.43	1.68	2.50
		CV	2.43	1.69	2.50
Non-linear effects	Single models	Cox-Lasso	2.02	1.03	2.08
		Cox-boosting	2.01	1.01	2.08
		RSF	1.89	1.15	1.95
	Flexible models	Stacking	1.97	1.00	2.04
		CV	2.01	1.03	2.07

Each simulation is replicated times, and the error is multiplied by . The two top estimators are bolded for each simulation scenario. ‘RSFs’ stand for random survival forests, ‘Stacking’ is stacked survival models, and ‘CV’ is the estimator selected through cross-validation. The standard error for the estimate ISSE for each method in each scenario is <0.005.