Zou Baiming, Cai Jianwen, Koch Gary G, Zhou Haibo, Zou Fei
Department of Biostatistics, University of Florida, Gainesville, FL 32611, USA.
Department of Biostatistics, University of North Carolina - Chapel Hill, Chapel Hill, NC 27599, USA.
Stat Med. 2017 Dec 30;36(30):4765-4776. doi: 10.1002/sim.7454. Epub 2017 Sep 4.
Conditional power based on summary statistic by comparing outcomes (such as the sample mean) directly between 2 groups is a convenient tool for decision making in randomized controlled trial studies. In this paper, we extend the traditional summary statistic-based conditional power with a general model-based assessment strategy, where the test statistic is based on a regression model. Asymptotic relationships between parameter estimates based on the observed interim data and final unobserved data are established, from which we develop an analytic model-based conditional power assessment for both Gaussian and non-Gaussian data. The model-based strategy is not only flexible in handling baseline covariates and more powerful in detecting the treatment effects compared with the conventional method but also more robust in controlling the overall type I error under certain missing data mechanisms. The performance of the proposed method is evaluated by extensive simulation studies and illustrated with an application to a clinical study.
基于两组间直接比较结果（如样本均值）的汇总统计量的条件效能，是随机对照试验研究中进行决策的便捷工具。在本文中，我们用一种基于通用模型的评估策略扩展了传统的基于汇总统计量的条件效能，其中检验统计量基于回归模型。建立了基于观察到的中期数据和最终未观察到的数据的参数估计之间的渐近关系，据此我们为高斯数据和非高斯数据开发了一种基于分析模型的条件效能评估方法。与传统方法相比，基于模型的策略不仅在处理基线协变量方面具有灵活性，在检测治疗效果方面更具效力，而且在某些缺失数据机制下控制总体I型错误时更稳健。通过广泛的模拟研究评估了所提方法的性能，并通过一项临床研究的应用进行了说明。
