Guedj M, Della-Chiesa E, Picard F, Nuel G
Laboratoire Statistique et Genome, 523 place des terrasses de l'Agora, 91000 Evry, France.
Ann Hum Genet. 2007 Mar;71(Pt 2):262-70. doi: 10.1111/j.1469-1809.2006.00316.x. Epub 2006 Oct 9.
In the framework of case-control studies many different test statistics are available to measure the association of a marker with a given disease. Nevertheless, choosing one particular statistic can lead to very different conclusions. In the absence of a consensus for this choice, a tempting option is to evaluate the power of these different statistics prior to make any decision. We review the available methods dedicated to power computation and assess their respective reliability in treating a wide range of tests on a wide range of alternative models. Considering Monte-Carlo, non-central chi-square and Delta-Method estimates, we evaluate empirical, asymptotic and numerical approaches. Additionally we introduce the use of the Delta-Method, extended to order 2, intended to provide better results than the traditional order-1 Delta-Method. Supplementary data can be found at: http://stat.genopole.cnrs.fr/software/dm2.
在病例对照研究的框架内,有许多不同的检验统计量可用于衡量一个标记物与特定疾病之间的关联。然而,选择一个特定的统计量可能会导致非常不同的结论。由于在这个选择上缺乏共识,一个诱人的选择是在做出任何决定之前评估这些不同统计量的检验效能。我们回顾了专门用于检验效能计算的可用方法,并评估了它们在处理广泛的替代模型上的各种检验时各自的可靠性。考虑到蒙特卡罗方法、非中心卡方估计和德尔塔方法估计,我们评估了经验性、渐近性和数值方法。此外,我们介绍了扩展到二阶的德尔塔方法的使用,旨在提供比传统的一阶德尔塔方法更好的结果。补充数据可在以下网址找到:http://stat.genopole.cnrs.fr/software/dm2 。