"Everything should be made as simple as possible, but not simpler." - Albert Einstein

Seminar Announcement

Inference on treatment effects from a randomized clinical trial in the presence of premature treatment discontinuation: The SYNERGY trial

Marie Davidian, Department of , NC State University

Monday, April 11, 2011

4:00 p.m., room 223, Weber Bldg

ABSTRACT

The Superior Yield of the New Strategy of Enoxaparin, Revascularization, and GlYcoprotein IIb/IIIa inhibitors (SYNERGY) trial was a randomized, open-label, multi-center clinical trial comparing two anticoagulant drugs (enoxaparin and unfractionated heparin, UFH) on the basis of various time-to-event endpoints.  In contrast to those of other studies of these agents, the primary, intent-to-treat analysis did not find sufficient evidence of a difference, leading to speculation that premature discontinuation of the study agents by some subjects might have attenuated the treatment effect.  As is of the case in such trials, some subjects discontinued (stopped or switched) their assigned treatment prematurely, either because occurrence of an adverse event or other condition under which discontinuation was mandated by the protocol or due to other reasons, e.g., switching to the other treatment at his/her provider's discretion (with more subjects switching from enoxaparin to UFH than vice versa).  In this situation, interest often focuses on "the difference in survival distributions had no subject discontinued his/her assigned treatment," inference on which is often attempted via standard analyses where event/censoring times for subjects discontinuing assigned treatment are artificially censored at the time of discontinuation.  However, this and other common ad hoc approaches may not yield reliable information because they are not based on a formal definition of the treatment effect of interest. We use SYNERGY as a context in which to describe how such an effect may be conceptualized properly and to present a statistical framework in which it may be identified, which leads naturally to the use of inverse probability weighted methods.

This is joint work with Min Zhang (University of Michigan), Butch Tsiatis (NCSU), and Karen Pieper and Ken Mahaffey (Duke Clinical Research Institute)