Table 7.
Performance of the proposed random forest estimator compared with other methods for L = 3, 4, 5.
| L | SRF | Naive.Cox | Naive.km | Lu.id | Lu.exp | Wang.id | Wang.exp |
|---|---|---|---|---|---|---|---|
| 3 | 0.6879 | 0.9247 | 0.9463 | 0.9266 | 0.9355 | 0.7630 | 0.7721 |
| 0.8258 | 0.8925 | 0.8967 | 0.8966 | 0.8983 | 0.8438 | 0.8455 | |
| 4 | 1.2033 | 1.5450 | 1.5686 | 1.5704 | 1.5777 | 1.2862 | 1.3044 |
| 1.2403 | 1.3597 | 1.3648 | 1.3830 | 1.3817 | 1.2719 | 1.2752 | |
| 5 | 1.7479 | 2.2107 | 2.2395 | 2.2467 | 2.2306 | 1.8251 | 1.8540 |
| 1.6761 | 1.8594 | 1.8655 | 1.8989 | 1.8858 | 1.7168 | 1.7193 |
The first row is MAE, the second row is RMSE. SRF, proposed random forest 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.