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Causal Per-protocol Analyses of Vaccine Trials

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):

Marco Carone  
University of Washington
Peter Gilbert  
Fred Hutchinson Cancer Research Center
Alex Luedtke  
University of Washington
Ted Westling  
University of Massachusetts Amherst

First Author:

Lujia Wang  
University of Massachusetts Amherst

Presenting Author:

Lujia Wang  
N/A

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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