Tarima Sergey, Flournoy Nancy
Institute for Health and Equity, Medical College of Wisconsin.
Department of Statistics, University of Missouri-Columbia.
Stat Pap (Berl). 2019 Apr;60(2):373-394. doi: 10.1007/s00362-019-01095-x. Epub 2019 Feb 28.
Consider an experiment in which the primary objective is to determine the significance of a treatment effect at a predetermined type I error and statistical power. Assume that the sample size required to maintain these type I error and power will be re-estimated at an interim analysis. A secondary objective is to estimate the treatment effect. Our main finding is that the asymptotic distributions of standardized statistics are random mixtures of distributions, which are non-normal except under certain model choices for sample size re-estimation (SSR). Monte-Carlo simulation studies and an illustrative example highlight the fact that asymptotic distributions of estimators with SSR may differ from the asymptotic distribution of the same estimators without SSR.
考虑这样一个实验，其主要目标是在预定的I型错误率和统计功效下确定治疗效果的显著性。假设在中期分析时将重新估计维持这些I型错误率和功效所需的样本量。次要目标是估计治疗效果。我们的主要发现是，标准化统计量的渐近分布是分布的随机混合，除了在样本量重新估计（SSR）的某些模型选择下，这些分布是非正态的。蒙特卡罗模拟研究和一个示例突出了这样一个事实，即具有SSR的估计量的渐近分布可能与没有SSR的相同估计量的渐近分布不同。
