Existing sequential generalized estimating equation methodology for longitudinal and group-correlated data focuses on narrow hypotheses concerning treatment efficacy and often makes modeling assumptions that impede the desirable robustness of the involved test statistics. Drawing upon the well-established theory of incremental information gain for well-posed sequential analyses, we develop an approach that does not rely on modeling assumptions that infringe upon the robustness of the resulting estimators while simultaneously testing a much wider range of hypotheses. Our methodology provides general submatrix-level asymptotic theory for the evaluation of joint covariance matrices of sequential test statistics. Moreover, this framework allows us to construct a novel approach to computing efficacy boundaries, the likes of which can be estimated with greater precision at later interim times. These constructions also accommodate accessible multiple imputation procedures, thereby allowing for our approach to be applied to incomplete datasets. Type I error and power are assessed through a series of comprehensive simulations mirroring the simulations of recent work to facilitate a proper comparison. We conclude by applying our methods to a dataset from a longitudinal study concerning the impact of race on the efficacy a treatment for hepatitis C.

翻译：现有的针对纵向和组相关数据的序贯广义估计方程方法主要关注治疗效果的狭义假设，且常采用有损检验统计量理想稳健性的建模假设。借鉴成熟序贯分析中增量信息增益的完善理论，我们提出了一种方法，该方法不依赖损害估计量稳健性的建模假设，同时能检验更广泛的假设。我们的方法为序贯检验统计量联合协方差矩阵的评估提供了通用的子矩阵级渐近理论。此外，该框架使我们能够构建一种计算疗效边界的新方法，此类边界在后期期中时间点可被更精确地估计。这些构造还兼容可行的多重插补程序，从而使我们的方法能够应用于不完整数据集。通过模拟近期研究的一系列综合仿真实验，我们评估了第一类错误与检验功效，以促进合理比较。最后，我们将所提方法应用于一项关于种族对丙型肝炎治疗效果影响的纵向研究数据集。