Combating the SARS-CoV2 pandemic will require the fast development of effective preventive vaccines. Regulatory agencies may open accelerated approval pathways for vaccines if an immunological marker can be established as a mediator of a vaccine's protection. A rich source of information for identifying such correlates are large-scale efficacy trials of COVID-19 vaccines, where immune responses are measured subject to a case-cohort sampling design. We propose two approaches to estimation of mediation parameters in the context of case-cohort sampling designs. We establish the theoretical large-sample efficiency of our proposed estimators and evaluate them in a realistic simulation to understand whether they can be employed in the analysis of COVID-19 vaccine efficacy trials.
A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes with application to a cardiovascular disease cohort study
Dealing with positivity violations in mediation analysis via weighted controlled effects, with application to assessing immune correlates of protection in antigen-experienced participants
Department of Statistics, University of Washington（华盛顿大学统计学系）；Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心疫苗与传染病部）
Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
McGill University（麦吉尔大学）；Research Institute of the McGill University Health Centre（麦吉尔大学健康中心研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心）；University of California, Berkeley（加州大学伯克利分校）
