Longini Ira M, Halloran M Elizabeth, Nizam Azhar
Department of Biostatistics, Rollins School of Public Health, Emory University, 1518 Clifton Road, N.E., Atlanta, GA 30322, USA.
Stat Med. 2002 Feb 28;21(4):481-95. doi: 10.1002/sim.994.
Community vaccine trials are becoming increasingly important to assess both the direct and indirect community level effects of vaccination. In this paper, we present statistical methods to analyse such trials, using a design with several matched pairs of communities. The communities are matched on similarities in infection transmission as reflected through the basic reproduction number. Two methods of analysis are presented and compared. The first is simple empirical estimation of vaccine effects. Summary measures of these effects are constructed by reciprocal variance weighted averages across the community pairs. The second is likelihood-based where we derive a mixed effects epidemic model. This model takes the intercommunity variability into account through a random effect on the basic reproduction number. With this model, we derive a distribution-free estimator for the variance of the random effect. We use simulated epidemics to explore the performance of the two estimation methods for different numbers of community pairs and different levels of inter-pair variability. Both methods provide acceptable estimates in terms of bias and precision under reasonable conditions. Although the empirical approach involves fewer assumptions than the model-based approach, the resulting vaccine effectiveness estimates are only applicable to the vaccination fraction tested in the trial. In contrast, the model-based approach can be used to predict the vaccine effectiveness at vaccination fractions other than those used in the trial. Thus, it can be used as a public health policy tool for predicting the community level effects of vaccination. We demonstrate such use by predicting total vaccine effectiveness for the whole range of vaccination fractions.
社区疫苗试验对于评估疫苗接种在社区层面的直接和间接影响变得越来越重要。在本文中，我们提出了统计方法来分析此类试验，采用了一种有几对匹配社区的设计。这些社区根据基本再生数所反映的感染传播相似性进行匹配。我们提出并比较了两种分析方法。第一种是对疫苗效果进行简单的经验估计。这些效果的汇总指标通过跨社区对的倒数方差加权平均值来构建。第二种是基于似然性的方法，我们推导了一个混合效应流行模型。该模型通过对基本再生数的随机效应来考虑社区间的变异性。利用这个模型，我们推导了随机效应方差的一个无分布估计量。我们使用模拟疫情来探索这两种估计方法在不同数量的社区对和不同水平的对间变异性情况下的性能。在合理条件下，两种方法在偏差和精度方面都能提供可接受的估计。尽管经验方法比基于模型的方法涉及的假设更少，但由此得到的疫苗效果估计仅适用于试验中测试的接种比例。相比之下，基于模型的方法可用于预测试验中未使用的接种比例下的疫苗效果。因此，它可以用作预测疫苗接种在社区层面影响的公共卫生政策工具。我们通过预测整个接种比例范围内的总疫苗效果来展示这种用途。
