An adjustment for a post-randomization variable in the comparison of two treatments for survival.
A method is proposed to infer the randomized treatment effect on survival after an adjustment for a post-randomization variable. The post-randomization variable is made independent of the treatment assignment and is considered a surrogate for baseline prognostic factors. The relationship between the post-randomization surrogate and baseline prognostic factors for survival is considered smooth, but otherwise unknown. The effect of these factors on survival is modelled using the Cox proportional hazards model with a semi-parametric relative risk function. Likelihood based estimates and tests for the treatment effect are demonstrated. An example from a randomized clinical trial in cancer illustrates the methodology.
Heller
G
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Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA. heller@biosta.mskcc.org
D000903:Antibiotics, Antineoplastic / Q000627:therapeutic use; D003630:Daunorubicin / Q000627:therapeutic use; D006801:Humans; D015255:Idarubicin / Q000627:therapeutic use; D015470:Leukemia, Myeloid, Acute / Q000188:drug therapy; D016013:Likelihood Functions; D008954:Models, Biological; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods*
D000903:Antibiotics, Antineoplastic; D015255:Idarubicin; D003630:Daunorubicin