Identifying biomarkers as surrogates for clinical endpoints in randomized vaccine trials is useful for reducing study duration and costs, relieving participants of unnecessary discomfort, and understanding vaccine-effect mechanism. In this article, we use risk models with multiple vaccine-induced immune response biomarkers to measure the causal association between a vaccine’s effects on these biomarkers and that on the clinical endpoint. In this setup, our main objective is to combine and select markers with high surrogacy from a list of many candidate markers, allowing us to get a more parsimonious model which can potentially increase the predictive quality of the true markers. To address the missing “potential” biomarker value if a subject receives placebo, we utilize the baseline immunogenicity predictor design augmented with a “closeout placebo vaccination” group. We then impute the missing potential marker values and conduct marker selection through a stepwise resampling and imputation method called stability selection. We test our proposed strategy under relevant simulation settings and on (partially simulated) biomarker data from a HIV vaccine trial (RV144).

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (