Zaihra Tasneem, Paul Sudhir
University of New Brunswick.
Int J Biostat. 2010;6(1):Article 35. doi: 10.2202/1557-4679.1177.
In epidemiological cohort studies, the probability of developing a disease for individuals in a treatment/intervention group is compared with that of a control group. The groups involve varying cluster sizes, and the binary responses within each cluster cannot be assumed independently. Three major measures of association used to report the efficacy of treatments or effectiveness of public health intervention programs in case of prospective studies are Risk Difference (RD), Risk Ratio (RR) and Relative Risk Difference (RED). The preference of one measure of association over the other in drawing statistical inference depends on design of study. Lui (2004) discusses a number of methods of constructing confidence intervals for each of these measures. Specifically, Lui (2004) discusses four methods for RD, four methods for RR and three methods for RED. For the construction of confidence intervals for RD, Paul and Zaihra (2008) compare the four methods discussed by Lui (2004), using extensive simulations with a method based on an estimator of the variance of a ratio estimator by Cochran (1977) and a method based on a sandwich estimator of the variance of the regression estimator using the generalized estimating equations approach of Zeger and Liang (1986). Paul and Zaihra (2008) conclude that the method based on an estimate of the variance of a ratio estimator performs best overall. In this paper, we extend the two new methodologies introduced in Paul and Zaihra (2008) to confidence interval construction of the risk measures RR and RED. Extensive simulations show that the method based on an estimate of the variance of a ratio estimator performs best overall for constructing confidence interval for the other two risk measures RR and RED as well. This method involves a very simple variance expression which can be implemented with a very few computer codes. Therefore, it can be considered as an easily implementable alternative for all the three measures of association.
在流行病学队列研究中，将治疗/干预组个体患某种疾病的概率与对照组个体的概率进行比较。这些组的聚类大小各不相同，并且不能假定每个聚类内的二元反应是独立的。在前瞻性研究中，用于报告治疗效果或公共卫生干预项目有效性的三个主要关联度量是风险差（RD）、风险比（RR）和相对风险差（RED）。在进行统计推断时，选择一种关联度量而非另一种关联度量取决于研究设计。Lui（2004）讨论了为这些度量中的每一个构建置信区间的多种方法。具体而言，Lui（2004）讨论了四种用于RD的方法、四种用于RR的方法和三种用于RED的方法。对于RD置信区间的构建，Paul和Zaihra（2008）比较了Lui（2004）讨论的四种方法，使用了基于Cochran（1977）的比率估计量方差估计器的方法以及基于Zeger和Liang（1986）的广义估计方程方法的回归估计量方差的三明治估计器的方法进行了广泛模拟。Paul和Zaihra（2008）得出结论，基于比率估计量方差估计的方法总体表现最佳。在本文中，我们将Paul和Zaihra（2008）中引入的两种新方法扩展到风险度量RR和RED的置信区间构建。广泛模拟表明，基于比率估计量方差估计的方法在构建其他两个风险度量RR和RED的置信区间时总体表现也最佳。该方法涉及一个非常简单的方差表达式，可用极少的计算机代码实现。因此，它可被视为所有三种关联度量的一种易于实现的替代方法。
