Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials.

ABSTRACT: When evaluating principal surrogate biomarkers in vaccine trials, missingness in potential outcomes requires prediction using auxiliary variables and/or augmented study design with a close-out placebo vaccination (CPV) component. The estimated likelihood approach, which separates the estimation of biomarker distribution from the maximization of the estimated likelihood, has often been adopted. Here we develop a likelihood-based approach that jointly estimates the two parts and describe the methods for selecting auxiliary variables as risk predictors and/or biomarker predictors. Through numerical studies, we observe that in a standard trial design without a CPV component, the two methods achieve similar performance in estimation of the risk model and the marker model. However, for trials au

SUBMITTER: Huang Y 

PROVIDER: S-EPMC7537791 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature