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Activity Number: 379
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #319678
Title: Addressing Subject-Level Heterogeneity in Sieve Analysis of Differential Vaccine Efficacy
Author(s): Jason Shao* and Paul T. Edlefsen
Companies: and Fred Hutchinson Cancer Research Center
Keywords: sieve analysis ; vaccine efficacy ; frailty models ; competing risks

In randomized trials of preventative vaccines, sieve analysis tests whether vaccine efficacy differs by a characteristic of the disease endpoint. These methods often assume a "leaky" model, in which treatment proportionally reduces the hazard of each disease type homogeneously in all subjects. Significant biases can occur in estimation and testing when this assumption does not hold. To allow for unobserved heterogeneity in participant response to vaccination, we propose a class of mixed models for sieve analysis incorporating subject-level random effects, known as frailty terms, into a competing hazards survival analysis framework. We show that all parameters in the model can be straightforwardly and reliably estimated using standard numeric optimization methods. In simulation studies, our approach performs favorably to existing methods, especially in cases where the leaky vaccine assumption is not appropriate. In some cases, large sample sizes are needed to distinguish between plausible sets of parameters in the frailty mixed model. We discuss implications for the design and interpretation of vaccine efficacy studies.

Authors who are presenting talks have a * after their name.

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