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Statistical Learning Methods to Determine Immune Correlates of Herpes Zoster in Vaccine Efficacy Trials
Peter B Gilbert1, Alexander R Luedtke1
1Department of Biostatistics, Bioinformatics, and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Abstract:
Using Super Learner, a machine learning statistical method, we assessed varicella zoster virus-specific glycoprotein-based enzyme-linked immunosorbent assay (gpELISA) antibody titer as an individual-level signature of herpes zoster (HZ) risk in the Zostavax Efficacy and Safety Trial. Gender and pre- and postvaccination gpELISA titers had moderate ability to predict whether a 50-59 year old experienced HZ over 1-2 years of follow-up, with equal classification accuracy (cross-validated area under the receiver operator curve = 0.65) for vaccine and placebo recipients. Previous analyses suggested that fold-rise gpELISA titer is a statistical correlate of protection and supported the hypothesis that it is not a mechanistic correlate of protection. Our results also support this hypothesis.
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