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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 4.

Simulation results: Mean (×103) and (sd) (×103). Censoring rate: 30%. 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) 1 (31) 2 (28) −10 (40) −20 (63) −26 (33)
Gaussian −17 (44) −10 (34) −7 (37) −18 (45) −48 (65)
2 Linear 22 (113) 17 (105) −14 (126) −110 (136) −193 (133) 65 (63)
Gaussian −39 (115) −25 (101) −62 (113) −164 (119) −285 (112)
3 Linear 785 (52) 768 (53) 771 (52) 737 (95) 667 (124) 763 (61)
Gaussian 896 (61) 810 (54) 854 (69) 817 (124) 679 (123)
4 Linear −453 (37) −465 (46) −448 (27) −461 (42) −471 (54) −457 (32)
Gaussian −465 (35) −477 (42) −456 (27) −474 (41) −505 (48)

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.