Statistical Learning Methods to Determine Immune Correlates of Herpes Zoster in Vaccine Efficacy Trials

J Infect Dis. 2018 Sep 22;218(suppl_2):S99-S101. doi: 10.1093/infdis/jiy421.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Antibodies, Viral / blood*
  • Area Under Curve
  • Case-Control Studies
  • Data Interpretation, Statistical
  • Female
  • Herpes Zoster / prevention & control*
  • Herpes Zoster Vaccine / immunology*
  • Herpes Zoster Vaccine / standards
  • Humans
  • Machine Learning*
  • Male
  • Middle Aged
  • Models, Statistical*
  • ROC Curve
  • Randomized Controlled Trials as Topic

Substances

  • Antibodies, Viral
  • Herpes Zoster Vaccine