“数字+”与统计数据工程系列讲座（四）9月22日Fred Hutchinson Cancer Center and University of Washington Ying Huang线上讲座预告

讲座题目：Evaluating Principal Surrogate Endpoints in Vaccine Trials in the Presence of Early Vaccine Efficacy

主 讲 人：Ying Huang

讲座时间：2022年9月22日（周四）上午10:00-11:00

地点：腾讯会议 ID 484-724-028

主讲人简介：

Ying Huang, Ph.D, is a full professor in the Biostatistics, Bioinformatics, and Epidemiology program at the Fred Hutchinson Cancer Center and an affiliate professor in the Department of Biostatistics at the University of Washington. Dr. Huang’s major areas of research are statistical methods for the design and analysis of biomarker studies aimed towards disease screening, diagnosis, treatment selection, and surrogate endpoint identification, with special focus on cancer and infectious disease prevention.  She was the PI of an NIH-funded R01 on “Statistical Methods for Selection and Evaluation of Biomarkers” (2013-2021), and is the PI of an NIH-funded U24 on “2/2 Ganciclovir to Prevent Reactivation of Cytomegalovirus in Patients with Acute Respiratory Failure and Sepsis”, and a co-investigator in the HIV Vaccine Trial Network (HVTN), the COVID-19 Prevention Network (CoVPN), the cancer Early Detection & Research Network (EDRN), and the Women’s Health Initiative (WHI). 

讲座摘要：

Identification of immune response biomarkers that can reliably predict a vaccine’s effect on the clinical endpoint plays a key role in vaccine research. 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 regardless of treatment received. Earlier work in principal surrogate evaluation relied on an ‘equal-early-clinical-risk’ assumption such that VE curve can be estimated 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. In this project 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 antibody titers in the motivating dengue and COVID-19 applications.