Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial.

Peter B Gilbert,Bryan S Blette,Bryan E Shepherd,Michael G Hudgens

doi:10.1515/jci-2019-0022

Abstract

While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal stratification framework (Frangakis and Rubin, 2002) for studying treatment effect modification by an intermediate response variable, using methods in the sub-field of principal surrogate (PS) analysis that studies multiple principal strata. Unfortunately, available methods for PS analysis require an augmented study design not available in HVTN 505, and make untestable structural risk assumptions, motivating a need for more robust PS methods. Fortunately, another sub-field of principal stratification, survivor average causal effect (SACE) analysis (Rubin, 2006) - which studies effects in a single principal stratum - provides many methods not requiring an augmented design and making fewer assumptions. We show how, for a binary intermediate response variable, methods developed for SACE analysis can be adapted to PS analysis, providing new and more robust PS methods. Application to HVTN 505 supports that the vaccine partially protected individuals with vaccine-induced T-cells expressing certain combinations of functions.

Highlights

A vaccine that prevents HIV-1 infection is critically needed for ending the global HIV-1 pandemic
A direct assessment of a causal vaccine effect would compare risk for each vaccinated subgroup defined by biomarker response value to that of the placebo recipient subgroup who would have had the same biomarker response value if assigned vaccination, which essentially repeats the primary analysis of vaccine efficacy across the marker-defined subgroups
We develop the methods under four scenarios of assumption sets– No Early Effect (NEE)-VB: A1–A5 hold and Variable Biomarker (VB) Sτ(0); NEE-CB: A1–A4 hold and Constant Biomarker (CB); No Early Harm (NEH)-CB : A1–A3, A4′ hold and Constant Biomarker; No Early Benefit (NEB)-CB : A1–A3, A4′′ hold and Constant Biomarker

Summary

Introduction

A vaccine that prevents HIV-1 infection is critically needed for ending the global HIV-1 pandemic. A direct assessment of a causal vaccine effect (eliminating possible selection bias) would compare risk for each vaccinated subgroup defined by biomarker response value to that of the placebo recipient subgroup who would have had the same biomarker response value if assigned vaccination, which essentially repeats the primary analysis of vaccine efficacy (which is valid based on the randomization) across the marker-defined subgroups. This “principal surrogate (PS) analysis" [3] is a sub-field of the general framework of principal stratification established by Frangakis and Rubin [3]

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Conclusion