Group-Sequential Analysis Incorporating Covariate Information
本文综述了在累积数据中拟合模型时参数估计序列的联合分布，提出了统一理论解释分组序贯检验统计量的独立增量结构，适用于正态线性模型、广义线性模型及生存数据的比例风险回归模型。
Abstract In this article we survey existing results concerning the joint distribution of the sequence of estimates of the parameter vector when a model is fitted to accumulating data and provide a unified theory that explains the “independent increments” structure commonly seen in group-sequential test statistics. Our theory covers normal linear models, including the case of correlated observations, and asymptotic results extend to generalized linear models and the proportional hazards regression model for survival data. The asymptotic results are derived using standard methods for the nonsequential case, and they hold as long as these nonsequential techniques are applicable at each individual analysis. In all cases, the joint distribution of the sequence of parameter estimates has the same form, exactly or asymptotically, as that of the sequence of means of an increasing number of independent, identically distributed normal variables. Thus our results provide the formal basis for extending the scope of standard group-sequential methods to a wide range of problems.