Department of Biostatistics, Bioinformatics, and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
J Infect Dis. 2018 Sep 22;218(suppl_2):S99-S101. doi: 10.1093/infdis/jiy421.
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.
我们使用 Super Learner 机器学习统计方法，评估水痘带状疱疹病毒特异性糖蛋白酶联免疫吸附试验 (gpELISA) 抗体滴度作为带状疱疹 (HZ) 风险的个体水平标志物，这是 Zostavax 功效和安全性试验的一部分。性别和疫苗接种前后 gpELISA 滴度在预测 50-59 岁人群在 1-2 年随访期间是否发生 HZ 方面具有中等能力，疫苗和安慰剂接受者的分类准确性相同（交叉验证受试者工作特征曲线下面积 = 0.65）。先前的分析表明，gpELISA 滴度的倍数增加是保护的统计学相关因素，并支持以下假设，即它不是保护的机制相关因素。我们的结果也支持这一假设。
