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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Heller Gdoi
10.1002/sim.968subject
Has Abstractpub_date
2001-11-30 00:00:00pages
3475-85issue
22eissn
0277-6715issn
1097-0258pii
10.1002/sim.968journal_volume
20pub_type
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