Gilbert P B, Self S G, Ashby M A
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Biometrics. 1998 Sep;54(3):799-814.
The human immunodeficiency virus type 1 (HIV-1) is extremely diverse. In assessing the utility of an HIV-1 vaccine, an important issue is the possibility of differential protection. We discuss statistical methods of inferring how the vaccine efficacy may vary with viral type from data that would be collected from a randomized, double-blind, placebo-controlled preventive vaccine efficacy trial. Detailed characterization of virus isolated from individuals infected during the trial will be available. We focus on the highly simplified case in which the viral characteristics are summarized by a single feature, which may be nominal, or a scalar quantity that represents distance between the isolate and the prototype virus or viruses used in the vaccine preparation. We consider discrete categorical and continuous response models for this quantity and identify models whose parameters can be interpreted as log ratios of strain-specific relative risks of infection in a prospective model for HIV-1 exposure and transmission. Methods of inference are described for the multinomial logistic regression (MLR) model for discrete categorical response, and a new semiparametric model which can be viewed as a continuous analog of the MLR model is introduced. The methods are illustrated by application to HIV-1 and hepatitis B vaccine trial data.
1型人类免疫缺陷病毒（HIV-1）具有极高的多样性。在评估HIV-1疫苗的效用时，一个重要问题是存在差异保护的可能性。我们讨论了从随机、双盲、安慰剂对照预防性疫苗效力试验收集的数据中推断疫苗效力如何随病毒类型变化的统计方法。将可获得对试验期间感染个体分离出的病毒的详细特征描述。我们聚焦于高度简化的情况，即病毒特征由单一特征概括，该特征可以是名义特征，或是表示分离株与疫苗制备中使用的原型病毒或多种原型病毒之间距离的标量。我们针对此数量考虑离散分类和连续响应模型，并识别其参数可在前瞻性HIV-1暴露和传播模型中解释为特定毒株感染相对风险对数比的模型。描述了针对离散分类响应的多项逻辑回归（MLR）模型的推断方法，并引入了一种新的半参数模型，该模型可视为MLR模型的连续类似物。通过应用于HIV-1和乙肝疫苗试验数据对这些方法进行了说明。
