Su J Q, Lachin J M
Department of Medical Statistics and Epidemiology, Mayo Clinic, Rochester, Minnesota 55905.
Biometrics. 1992 Dec;48(4):1033-42.
Many studies involve the collection of multivariate observations, such as repeated measures, on two groups of subjects who are recruited over time, i.e., with staggered entry of subjects. Various marginal distribution-free multivariate methods have been proposed for the analyses of such multivariate observations where some measures may be missing at random. Using the multivariate U statistic of Wei and Johnson (1985, Biometrika 72, 359-364), we describe the group sequential analysis of such a study where the multivariate observations are observed sequentially--both within and among subjects. We describe a multivariate generalization of the Hodges and Lehmann (1963, Annals of Mathematical Statistics 34, 598-611) estimator of a location shift that can be obtained via the multivariate U statistic with the Mann-Whitney-Wilcoxon kernel. We then describe large-sample group sequential interval estimators and tests based on an aggregate estimate of the location shift combined over all of the repeated measures. We also describe how the same steps could be employed to perform a group sequential analysis based on any one of the variety of marginal multivariate methods that have been proposed. These methods are applied to a real-life example.
许多研究涉及对两组随时间招募的受试者（即受试者交错进入）进行多变量观测的收集，例如重复测量。针对此类多变量观测的分析，已经提出了各种无边缘分布的多变量方法，其中一些测量值可能会随机缺失。利用Wei和Johnson（1985年，《生物统计学》72卷，359 - 364页）的多变量U统计量，我们描述了对这样一项研究的组序贯分析，其中多变量观测是在受试者内部和受试者之间依次进行观测的。我们描述了一种通过具有Mann - Whitney - Wilcoxon核的多变量U统计量可获得的位置偏移的Hodges和Lehmann（1963年，《数理统计年鉴》34卷，598 - 611页）估计量的多变量推广。然后，我们基于对所有重复测量的位置偏移的综合估计，描述了大样本组序贯区间估计量和检验。我们还描述了如何采用相同的步骤，基于已提出的各种边缘多变量方法中的任何一种来进行组序贯分析。这些方法应用于一个实际例子。
