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## 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. Copyright © 2001 John Wiley & Sons, Ltd.

Who reads An Adjustment for a Post‐randomization Variable in the Comparison of Two Treatments for Survival?

Author

Glenn Heller

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0277-6715)

Published

2001

Language

EN

Field

Mathematics (Physical Sciences)