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

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  • Gilbert Peter B.

    (Department of Biostatistics, University of Washington and Fred Hutchinson Cancer Research Center, Seattle, Washington, 98109, United States of America)

  • Blette Bryan S.

    (Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, 27599, United States of America)

  • Shepherd Bryan E.

    (Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, 37232, United States of America)

  • Hudgens Michael G.

    (Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, 27599, United States of America)

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.

Suggested Citation

  • Gilbert Peter B. & Blette Bryan S. & Shepherd Bryan E. & Hudgens Michael G., 2020. "Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 54-69, January.
    • Handle: RePEc:bpj:causin:v:8:y:2020:i:1:p:54-69:n:3
    DOI: 10.1515/jci-2019-0022
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    References listed on IDEAS

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