Biostatistics Seminar Series - Ying Huang, PhD

Title:  Sensitivity analysis for evaluating principal surrogate endpoints in vaccine trials relaxing the equal early clinical risk assumptionAbstract:  Immune response biomarkers that can reliably predict a vaccine's effect on a clinical endpoint of interest are important for guiding vaccine development. A common metric for quantifying a biomarker's principal surrogacy is the vaccine efficacy (VE) 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 VE 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. Our current research was motivated by the CYD-TDV tetravalent dengue vaccine's early protective effect against virologically confirmed dengue, observed in two phase III dengue vaccine trials (CYD14, CYD15). A similar issue exists in COVID-19 preventive VE trials. 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 VE curve estimators based on the estimated maximum likelihood approach. We use the proposed methodology to assess the surrogacy of post-randomization neutralization titer in the motivating dengue application.