# Application of Causal Inference to Establish Assay Effect in the Absence of a Bridging Study: A Case Study of MenACWY‐CRM Conjugate Vaccine Data

## TL;DR

This paper explores using causal inference to compare vaccine immunogenicity data when assay changes prevent direct comparison, using a meningococcal vaccine case study.

## Contribution

The paper introduces novel application of overlap weighting to address assay bridging in vaccine development without a bridging study.

## Key findings

- Overlap weighting resolved extreme weights caused by poor covariate overlap between groups.
- Propensity score-based methods effectively estimated assay bridging effects in the absence of a bridging study.
- Automated method selection requires caution due to differences in estimands targeted by various techniques.

## Abstract

During a vaccine development program, if the assay used to measure immunological endpoints is changed, ideally, a bridging study is performed to establish the relationship between results obtained with the new and previous assay. However, this is not always feasible, and when bridging study data are absent, this can limit the ability to use historical study information to strengthen evidence generated in the clinical program. We present a case study on GSK's quadrivalent meningococcal vaccine (MenACWY‐CRM), where the immunogenicity assay was changed over time. A large amount of study data was collected in randomized controlled clinical trials, providing a valuable source of information to support vaccine development, but the introduction of the new assay complicated the comparison of antibody responses across studies. Several causal inference techniques, developed for the analysis of non‐randomized studies, can be used to estimate the assay bridging effect and, as observed in our case study, address the presence of confounding factors resulting from pooling group data from different sources. Cutting‐edge propensity score‐based methods were evaluated, highlighting their advantages and limitations. Within the family of propensity score weighting methods, the widely used inverse probability weighting was compared to the novel overlap weighting technique. The latter was shown to resolve the problem of extreme weights in a situation where there was poor overlap in covariate distribution between two groups. Automated selection of specific methods should be approached with caution, carefully considering the different estimands targeted by different methods.