Variant Specific Treatment Effects with Applications in Vaccine Studies

TL;DR

This paper develops a formal causal inference framework for analyzing variant-specific treatment effects, especially in vaccine studies with interference, providing clearer interpretation and new estimands.

Contribution

It introduces a formal framework for variant-specific effects considering interference, clarifies existing methods, and proposes new estimands for vaccine efficacy analysis.

Findings

Derived conditions for causal interpretation of vaccine efficacy parameters

Justified reporting of relative vaccine effects over absolute effects

Applied framework to HIV vaccine trial data

Abstract

Pathogens usually exist in heterogeneous variants, like subtypes and strains. Quantifying treatment effects on the different variants is important for guiding prevention policies and treatment development. Here we ground analyses of variant-specific effects on a formal framework for causal inference. This allows us to clarify the interpretation of existing methods and define new estimands. Unlike most of the existing literature, we explicitly consider the (realistic) setting with interference in the target population: even if individuals can be sensibly perceived as iid in randomized trial data, there will often be interference in the target population where treatments, like vaccines, are rolled out. Thus, one of our contributions is to derive explicit conditions guaranteeing that commonly reported vaccine efficacy parameters quantify well-defined causal effects, also in the presence of interference. Furthermore, our results give alternative justifications for reporting estimands on the relative, rather than absolute, scale. We illustrate the findings with an analysis of a large HIV1 vaccine trial, where there is interest in distinguishing vaccine effects on viruses with different genome sequences.