Semiparametric Efficiency and Its Implication on the Design and Analysis of Group-Sequential Studies
本文证明高效检验统计量的极限分布是多元正态且具有独立增量协方差结构，并基于此提出适用于任何成组序贯研究的信息化设计与监测方法。
Abstract Authors have shown that the time-sequential joint distributions of many statistics used to analyze data arising from group-sequential time-to-event and longitudinal studies are multivariate normal with an independent increments covariance structure. In Theorem 1 of this article, we demonstrate that this limiting distribution arises naturally when one uses an efficient test statistic to test a single parameter in a semiparametric or parametric model. Because we are able to think of many of the statistics in the literature in this fashion, the limiting distribution under investigation is just a special case of Theorem 1. Using this general structure, we then develop an information-based design and monitoring procedure that can be applied to any type of model for any type of group-sequential study provided that there is a unique parameter of interest that can be efficiently tested.