Evaluating the surrogacy of multiple vaccine-induced immune response biomarkers in HIV vaccine trials. (21st October 2019)

• Record Type: Journal Article
• Authors: Dasgupta, Sayan; Huang, Ying
• Is Part Of: Biostatistics. Volume 22:Number 2(2021)
• Page Start: 421
• Page End: 436
• Publication Date: 2019-10-21
• Subjects: Biomarkers -- BIP design -- BIP + CPV design -- Identifiability -- Stability selection; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxz039
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: 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).