Evaluation of Treatment Effect Modification by Biomarkers Measured Pre- and Post-randomization in the Presence of Non-monotone Missingness

TL;DR

This paper develops a new statistical method to analyze how biomarkers measured before and after treatment influence vaccine efficacy, especially when baseline biomarker data are missing for some participants, demonstrated through dengue vaccine trials.

Contribution

The authors propose an estimated likelihood approach for effect modification analysis using sub-sampled baseline biomarker data, addressing missing data issues in vaccine studies.

Findings

Method successfully applied to dengue vaccine trial data.

Improves understanding of biomarker effects on vaccine efficacy.

Handles non-monotone missingness in baseline biomarker measurements.

Abstract

In vaccine studies, investigators are often interested in studying effect modifiers of clinical treatment efficacy by biomarker-based principal strata, which is useful for selecting biomarker study endpoints for evaluating treatments in new trials, exploring biological mechanisms of clinical treatment efficacy, and studying mediators of clinical treatment efficacy. However, in trials where participants may enter the study with prior exposure therefore with variable baseline biomarker values, clinical treatment efficacy may depend jointly on a biomarker measured at baseline and measured at a fixed time after vaccination. Therefore, it is of interest to conduct a bivariate effect modification analysis by biomarker-based principal strata and baseline biomarker values. Previous methods allow this assessment if participants who have the biomarker measured at the the fixed time point post randomization would also have the biomarker measured at baseline. However, additional complications in study design could happen in practice. For example, in the Dengue correlates study, baseline biomarker values were only available from a fraction of participants who have biomarkers measured post-randomization. How to conduct the bivariate effect modification analysis in these studies remains an open research question. In this article, we propose an estimated likelihood method to utilize the sub-sampled baseline biomarker in the effect modification analysis and illustrate our method with datasets from two dengue phase 3 vaccine efficacy trials.