Abstract Number:
2176
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Lujia Wang (1), Marco Carone (2), Peter Gilbert (3), Alex Luedtke (2), Ted Westling (1)
Institutions:
(1) University of Massachusetts Amherst, Amherst, MA, US, (2) University of Washington, Seattle, WA, US, (3) Fred Hutchinson Cancer Research Center, Seattle, WA, US
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Per-protocol analyses of vaccine efficacy trials typically compare event rates between participants assigned to vaccine and placebo among those who adhered to the trial protocol. However, conditioning on adherence introduces the potential for confounding bias because it occurs post-randomization. In this work, we present the goals of per-protocol analyses in vaccine efficacy trials using the Neyman-Rubin causal model. We define three effects: the intention-to-treat effect, the per-protocol cohort effect, and the causal per-protocol effect. We present the correct interpretation of these three effects, and weigh their pros and cons as effects of interest in the analysis of vaccine trials. We then introduce estimators of these three effects, focusing in particular on estimation of the causal per-protocol effect under a no unobserved confounding assumption using Inverse Probability of Treatment Weighting and Longitudinal Targeted Maximum Likelihood Estimation. We use simulation studies to demonstrate how non-adherence, confounding, and effect modification influence when these estimators can be used to make reliable conclusions about the causal effect of protocol adherence.
Keywords:
Causal Inference|Per-protocol analyses|Vaccine trials|Inverse probability of treatment weighting |Longitudinal targeted maximum likelihood estimation|
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Causal Inference
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