Table 6.

Comparison of Mean-Absolute-Error (MAE) and Rooted-Mean-Squared-Error (RMSE) for Model 2 with different link functions and the censoring distribution is mis-specificed with α = 1.5.

p	SRF	Naive.Cox	Naive.km	Lu.id	Lu.exp	Wang.id	Wang.exp
Model 1: identity link, n = 3, 000, SNR = 0.3
5	0.1227	0.1396	0.1395	0.1397	0.1397	0.1394	0.1394
	0.1507	0.1666	0.1664	0.1668	0.1668	0.1666	0.1666
10	0.1241	0.1391	0.1389	0.1393	0.1393	0.1390	0.1390
	0.1514	0.1667	0.1664	0.1669	0.1669	0.1668	0.1668
20	0.1232	0.1390	0.1386	0.1393	0.1393	0.1389	0.1389
	0.1499	0.1659	0.1654	0.1663	0.1663	0.1661	0.1661
Model 1: log-exp link, n = 3, 000, SNR = 0.3
5	0.1210	0.1376	0.1375	0.1378	0.1378	0.1374	0.1374
	0.1487	0.1643	0.1642	0.1645	0.1645	0.1643	0.1643
10	0.1224	0.1372	0.1370	0.1374	0.1374	0.1371	0.1371
	0.1494	0.1644	0.1642	0.1646	0.1646	0.1645	0.1645
20	0.1215	0.1371	0.1368	0.1374	0.1374	0.1370	0.1370
	0.1480	0.1637	0.1632	0.1641	0.1641	0.1638	0.1638
Model 1: exp link, n = 3, 000, SNR = 0.3
5	21.071	23.719	23.699	23.787	23.785	23.581	23.580
	26.092	28.241	28.217	28.313	28.311	28.238	28.238
10	21.334	23.649	23.612	23.711	23.710	23.524	23.524
	26.159	28.231	28.186	28.283	28.281	28.224	28.224
20	21.176	23.629	23.571	23.748	23.745	23.492	23.492
	25.893	28.077	27.993	28.208	28.204	28.085	28.085

The number of covariates p = 5, 10, 20, for each p, the first row is MAE, the second row is RMSE. SRF, proposed random forest-bases estimator; Naive.km, estimate based on Kaplan–Meier estimator without adjusting for the covariates; Naive.Cox, Cox regression based estimator; Lu.id, method of Tian et al. (2014) with identity link; Lu.exp, method of Tian et al. (2014) with exponential link; Wang.id, method of Wang and Schaubel (2018) with identity link; Wang:exp, method of Wang and Schaubel (2018) with exponential link.