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. Author manuscript; available in PMC: 2018 Oct 18.
Published in final edited form as: Electron J Stat. 2017 Oct 18;11(2):3927–3953. doi: 10.1214/17-EJS1305

Table 1.

Simulation results: Mean (×103) and (sd) (×103). Censoring rate: 45%. For each scenario, the theoretical optimal value (×103) is 31, 181, 1079, and −389, respectively.

kernel T RIST-R1 RIST-R2 ICO DR Cox
1 Linear 0 (26) 0 (31) 1 (30) −20 (54) −39 (76) −29 (33)
Gaussian −17 (44) −11 (35) −8 (36) −25 (50) −88 (79)
2 Linear 22 (113) −1 (112) −24 (125) −137 (131) −232 (132) 53 (69)
Gaussian −39 (115) −40 (103) −72 (114) −175 (120) −311 (106)
3 Linear 785 (52) 766 (59) 763 (51) 683 (113) 598 (120) 745 (64)
Gaussian 896 (61) 803 (56) 834 (71) 785 (105) 606 (115)
4 Linear −453 (37) −469 (47) −451 (27) −469 (48) −481 (59) −464 (36)
Gaussian −465 (35) −482 (44) −457 (28) −487 (45) −531 (43)

T: using true survival time as weight; RIST-R1 and RIST-R2: using the estimated R1 and R2 respectively as weights, while the conditional expectations are estimated using recursively imputed survival trees; ICO: inverse probability of censoring weighted learning; DR: doubly robust outcome weighted learning; Cox: Cox proportional hazards model using covariate-treatment interactions.