Nolen Tracy L, Hudgens Michael G, Senb Pranab K, Koch Gary G
RTI International, The Research Triangle Park, NC, 27709, U.S.A.
Stat Med. 2015 May 30;34(12):1981-92. doi: 10.1002/sim.6462. Epub 2015 Mar 9.
Preclinical evaluation of candidate human immunodeficiency virus (HIV) vaccines entails challenge studies whereby non-human primates such as macaques are vaccinated with either an active or control vaccine and then challenged (exposed) with a simian-version of HIV. Repeated low-dose challenge (RLC) studies in which each macaque is challenged multiple times (either until infection or some maximum number of challenges is reached) are becoming more common in an effort to mimic natural exposure to HIV in humans. Statistical methods typically employed for the testing for a vaccine effect in RLC studies include a modified version of Fisher's exact test as well as large sample approaches such as the usual log-rank test. Unfortunately, these methods are not guaranteed to provide a valid test for the effect of vaccination. On the other hand, valid tests for vaccine effect such as the exact log-rank test may not be easy to implement using software available to many researchers. This paper details which statistical approaches are appropriate for the analysis of RLC studies, and how to implement these methods easily in SAS or R.
候选人类免疫缺陷病毒（HIV）疫苗的临床前评估需要进行攻毒研究，即对猕猴等非人灵长类动物接种活性疫苗或对照疫苗，然后用猿猴版本的HIV进行攻毒（暴露）。重复低剂量攻毒（RLC）研究中，每只猕猴接受多次攻毒（要么直到感染，要么达到最大攻毒次数），这种研究正变得越来越普遍，旨在模拟人类自然接触HIV的情况。RLC研究中通常用于检验疫苗效果的统计方法包括费舍尔精确检验的修正版本以及大样本方法，如常用的对数秩检验。不幸的是，这些方法并不能保证为疫苗接种效果提供有效的检验。另一方面，像精确对数秩检验这样的有效疫苗效果检验，使用许多研究人员可用的软件可能不容易实现。本文详细介绍了哪些统计方法适用于RLC研究的分析，以及如何在SAS或R中轻松实现这些方法。
