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当在预先计划的中期观察中调整样本量和分配率时,Ⅰ类错误率的最大膨胀。

Maximum inflation of the type 1 error rate when sample size and allocation rate are adapted in a pre-planned interim look.

机构信息

Center for Medical Statistics, Informatics and Intelligent Systems, Spitalgasse 23, 1090 Vienna, Austria.

出版信息

Stat Med. 2011 Jun 30;30(14):1637-47. doi: 10.1002/sim.4230. Epub 2011 Apr 15.

Abstract

We calculate the maximum type 1 error rate of the pre-planned conventional fixed sample size test for comparing the means of independent normal distributions (with common known variance) which can be yielded when sample size and allocation rate to the treatment arms can be modified in an interim analysis. Thereby it is assumed that the experimenter fully exploits knowledge of the unblinded interim estimates of the treatment effects in order to maximize the conditional type 1 error rate. The 'worst-case' strategies require knowledge of the unknown common treatment effect under the null hypothesis. Although this is a rather hypothetical scenario it may be approached in practice when using a standard control treatment for which precise estimates are available from historical data. The maximum inflation of the type 1 error rate is substantially larger than derived by Proschan and Hunsberger (Biometrics 1995; 51:1315-1324) for design modifications applying balanced samples before and after the interim analysis. Corresponding upper limits for the maximum type 1 error rate are calculated for a number of situations arising from practical considerations (e.g. restricting the maximum sample size, not allowing sample size to decrease, allowing only increase in the sample size in the experimental treatment). The application is discussed for a motivating example.

摘要

我们计算了在中期分析中可以修改样本量和治疗臂分配率的情况下,比较独立正态分布(具有共同已知方差)均值的预先计划的常规固定样本量检验的最大类型 1错误率。因此,假设实验者充分利用了对治疗效果的未盲中期估计的知识,以最大化条件类型 1错误率。“最坏情况”策略需要在零假设下对未知的共同治疗效果有所了解。尽管这是一个相当假设的情况,但在使用标准对照治疗时,从历史数据中可以获得精确的估计,这种情况在实践中可能会出现。与 Proschan 和 Hunsberger(Biometrics 1995; 51:1315-1324)为在中期分析前后应用平衡样本的设计修改推导的类型 1错误率膨胀相比,这种类型 1错误率的膨胀要大得多。针对实际考虑因素(例如限制最大样本量、不允许样本量减少、仅允许实验治疗中样本量增加)出现的多种情况计算了最大类型 1错误率的上限。讨论了一个有启发性的例子的应用。

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