Mercante D E, Johnson W D
Department of Biometry and Genetics, Louisiana State University Medical Center, New Orleans 70112-1393.
J Biopharm Stat. 1993 Sep;3(2):141-52. doi: 10.1080/10543409308835055.
This article discusses statistical methods for the analysis of multivariate data arising in clinical trials involving a small number of subjects randomly assigned to one of several treatment groups. Possible violations of traditional assumptions such as variance homogeneity and normality of errors are often dealt with by carrying out the statistical analysis using strategies such as transforming the data or applying nonparametric procedures. Multivariate nonparametric tests provide a realistic alternative for analyzing such data. We present a permutation procedure for analyzing data arising in randomized experiments.
本文讨论了用于分析临床试验中产生的多变量数据的统计方法，这些试验涉及少量随机分配到几个治疗组之一的受试者。对于诸如方差齐性和误差正态性等传统假设可能的违背情况，通常通过使用诸如转换数据或应用非参数程序等策略来进行统计分析。多变量非参数检验为分析此类数据提供了一种切实可行的替代方法。我们提出了一种用于分析随机实验中产生的数据的排列程序。
