Causal inference methods for vaccine sieve analysis with effect modification

Abstract

The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen's characteristics, which are measured postrandomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this article, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, nonrandomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.

Keywords: malaria; marginal structural models; sieve analysis; targeted minimum loss-based estimation; vaccines.