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Updated: Oct 3, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
An adjustment for a post-randomization variable in the comparison of two treatments for survival
1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA. heller@biosta.mskcc.org
Abstract:
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.
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