Sieve analysis in HIV-1 vaccine efficacy trials

aStatistical Center for HIV/AIDS Research and Prevention, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington bU.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring cHenry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA.
Current opinion in HIV and AIDS (Impact Factor: 4.68). 05/2013; 8(5). DOI: 10.1097/COH.0b013e328362db2b
Source: PubMed


Purpose of review:
The genetic characterization of HIV-1 breakthrough infections in vaccine and placebo recipients offers new ways to assess vaccine efficacy trials. Statistical and sequence analysis methods provide opportunities to mine the mechanisms behind the effect of an HIV vaccine.

Recent findings:
The release of results from two HIV-1 vaccine efficacy trials, Step/HVTN-502 (HIV Vaccine Trials Network-502) and RV144, led to numerous studies in the last 5 years, including efforts to sequence HIV-1 breakthrough infections and compare viral characteristics between the vaccine and placebo groups. Novel genetic and statistical analysis methods uncovered features that distinguished founder viruses isolated from vaccinees from those isolated from placebo recipients, and identified HIV-1 genetic targets of vaccine-induced immune responses.

Studies of HIV-1 breakthrough infections in vaccine efficacy trials can provide an independent confirmation to correlates of risk studies, as they take advantage of vaccine/placebo comparisons, whereas correlates of risk analyses are limited to vaccine recipients. Through the identification of viral determinants impacted by vaccine-mediated host immune responses, sieve analyses can shed light on potential mechanisms of vaccine protection.

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Available from: Paul Edlefsen, Feb 09, 2015
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    • "In addition to evaluating vaccine efficacy as a function of subject-specific covariates, it is often of interest to evaluate the extent to which a vaccine's efficacy differs by type of infection. In a series of papers on what has variously been called “mark-specific intervention efficacy” or “sieve effects,” Gilbert et al. defined sufficient conditions under which estimates are unbiased for quantities relevant to the identification of these effects [5–7]. Here we argue that one of those conditions can be represented as a requirement of “proportional exposure pseudohazards” and that this condition is required not only for the failure-type-only methods (such as multinomial logistic regression (MLR)) but also for the time-to-event methods (including competing risks Cox models, even when relaxing the assumption of proportional baseline risks as in [8]). "
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