Table 1.

Comparison of Mean-Absolute-Error (MAE) and Rooted-Mean-Squared-Error (RMSE) for Model 1 with different link functions.

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	0.1359	0.1371	0.2067	0.1341	0.1346	0.1341	0.1346
	0.1699	0.1695	0.2466	0.1687	0.1691	0.1686	0.1691
10	0.1396	0.1394	0.2108	0.1371	0.1377	0.1371	0.1376
	0.1721	0.1710	0.2497	0.1710	0.1715	0.1709	0.1714
20	0.1373	0.1372	0.2064	0.1342	0.1348	0.1342	0.1347
	0.1703	0.1693	0.2464	0.1686	0.1691	0.1685	0.1690
Model 1: log-exp link, n = 3, 000, SNR = 0.3
5	0.1347	0.1359	0.2048	0.1330	0.1335	0.1330	0.1335
	0.1684	0.1680	0.2441	0.1673	0.1677	0.1672	0.1677
10	0.1384	0.1382	0.2088	0.1359	0.1366	0.1359	0.1365
	0.1706	0.1695	0.2472	0.1695	0.1701	0.1695	0.1699
20	0.1361	0.1360	0.2044	0.1331	0.1337	0.1330	0.1336
	0.1689	0.1679	0.2439	0.1672	0.1678	0.1671	0.1676
Model 1: exp link, n = 3, 000, SNR = 0.3
5	24.724	25.398	33.688	24.496	24.723	24.436	24.709
	30.827	30.860	39.296	30.608	30.773	30.577	30.749
10	25.254	25.681	34.208	24.843	25.162	24.812	25.149
	31.085	31.052	39.621	30.869	31.076	30.850	31.048
20	24.878	25.260	33.587	24.390	24.679	24.325	24.651
	30.744	30.695	39.181	30.479	30.689	30.438	30.646

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.