Kang Chaeryon, Huang Ying
Department of Biostatistics, University of Pittsburgh.
Vaccine and Infectious Disease Division and Public Health Sciences Division, Fred Hutchinson Cancer Research Center.
Ann Appl Stat. 2023 Jun;17(2):1199-1219. doi: 10.1214/22-aoas1665. Epub 2023 May 1.
In HIV vaccine/prevention research, probing into the vaccine-induced immune responses that can help predict the risk of HIV infection provides valuable information for the development of vaccine regimens. Previous correlate analysis of the Thai vaccine trial aided the discovery of interesting immune correlates related to the risk of developing an HIV infection. The present study aimed to identify the combinations of immune responses associated with the heterogeneous infection risk. We explored a "change-plane" via combination of a subset of immune responses that could help separate vaccine recipients into two heterogeneous subgroups in terms of the association between immune responses and the risk of developing infection. Additionally, we developed a new variable selection algorithm through a penalized likelihood approach to investigate a parsimonious marker combination for the change-plane. The resulting marker combinations can serve as candidate correlates of protection and can be used for predicting the protective effect of the vaccine against HIV infection. The application of the proposed statistical approach to the Thai trial has been presented, wherein the marker combinations were explored among several immune responses and antigens.
在HIV疫苗/预防研究中,探究有助于预测HIV感染风险的疫苗诱导免疫反应,可为疫苗方案的开发提供有价值的信息。先前泰国疫苗试验的相关性分析有助于发现与HIV感染风险相关的有趣免疫相关性。本研究旨在确定与异质性感染风险相关的免疫反应组合。我们通过组合一部分免疫反应探索了一个“变化平面”,这部分免疫反应有助于根据免疫反应与感染风险之间的关联将疫苗接种者分为两个异质性亚组。此外,我们通过惩罚似然法开发了一种新的变量选择算法,以研究该变化平面的简约标记组合。所得的标记组合可作为保护的候选相关因素,并可用于预测疫苗对HIV感染的保护作用。本文介绍了所提出的统计方法在泰国试验中的应用,其中在几种免疫反应和抗原中探索了标记组合。