Sensitivity analysis for evaluating principal surrogate endpoints relaxing the equal early clinical risk assumption
Ying Huang, Yingying Zhuang, Peter Gilbert
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 3
Abstract
This article addresses the evaluation of postrandomization immune response biomarkers as principal surrogate endpoints of a vaccine’s protective effect, based on data from randomized vaccine trials. An important metric for quantifying a biomarker’s principal surrogacy in vaccine research is the vaccine efficacy curve, which shows a vaccine’s efficacy as a function of potential biomarker values if receiving vaccine, among an “early-always-at-risk” principal stratum of trial participants who remain disease-free at the time of biomarker measurement whether having received vaccine or placebo. Earlier work in principal surrogate evaluation relied on an “equal-early-clinical-risk” assumption for identifiability of the vaccine curve, based on observed disease status at the time of biomarker measurement. This assumption is violated in the common setting that the vaccine has an early effect on the clinical endpoint before the biomarker is measured. In particular, a vaccine’s early protective effect observed in two phase III dengue vaccine trials (CYD14/CYD15) has motivated our current research development. We relax the “equal-early-clinical-risk” assumption and propose a new sensitivity analysis framework for principal surrogate evaluation allowing for early vaccine efficacy. Under this framework we develop inference procedures for vaccine efficacy curve estimators, based on the estimated maximum likelihood approach. We then use the proposed methodology to assess the surrogacy of postrandomization neutralization titer in the motivating dengue application.
Keywords
Causal inference, estimated maximum likelihood, Principal stratification, principal surrogate, sensitivity analysis, vaccine efficacy curve
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