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最优、最小最大化和可接受的两阶段设计用于肿瘤学 II 期临床试验。

Optimal, minimax and admissible two-stage design for phase II oncology clinical trials.

机构信息

Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.

Department of Biostatistics, School of Public Health, Nanjing Medical University, SPH Building Room 418, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.

出版信息

BMC Med Res Methodol. 2020 May 20;20(1):126. doi: 10.1186/s12874-020-01017-8.

Abstract

BACKGROUND

The article aims to compare the efficiency of minimax, optimal and admissible criteria in Simon's and Fleming's two-stage design.

METHODS

Three parameter settings (p-p = 0.25-0.05, 0.30-0.10, 0.50-0.30) are designed to compare the maximum sample size, the critical values and the expected sample size for minimax, optimal and admissible designs. Type I & II error constraints (α, β) vary across (0.10, 0.10), (0.05, 0.20) and (0.05, 0.10), respectively.

RESULTS

In both Simon's and Fleming's two-stage designs, the maximum sample size of admissible design is smaller than optimal design but larger than minimax design. Meanwhile, the expected samples size of admissible design is smaller than minimax design but larger than optimal design. Mostly, the maximum sample size and expected sample size in Fleming's designs are considerably smaller than that of Simon's designs.

CONCLUSIONS

Whenever (p, p) is pre-specified, it is better to explore in the range of probability q, based on relative importance between maximum sample size and expected sample size, and determine which design to choose. When q is unknown, optimal design may be more favorable for drugs with limited efficacy. Contrarily, minimax design is recommended if treatment demonstrates impressive efficacy.

摘要

背景

本文旨在比较 Simon 两阶段设计和 Fleming 两阶段设计中极小极大、最优和可接受标准的效率。

方法

设计了三种参数设置(p-p=0.25-0.05、0.30-0.10、0.50-0.30),用于比较极小极大、最优和可接受设计的最大样本量、临界值和预期样本量。I 型和 II 型误差约束(α,β)分别在(0.10,0.10)、(0.05,0.20)和(0.05,0.10)之间变化。

结果

在 Simon 两阶段设计和 Fleming 两阶段设计中,可接受设计的最大样本量均小于最优设计,但大于极小极大设计。同时,可接受设计的预期样本量小于极小极大设计,但大于最优设计。大多数情况下,Fleming 设计的最大样本量和预期样本量明显小于 Simon 设计。

结论

无论(p,p)是否预先指定,都最好根据最大样本量和预期样本量之间的相对重要性,在概率 q 的范围内进行探索,并确定选择哪种设计。当 q 未知时,最优设计可能更有利于疗效有限的药物。相反,如果治疗效果显著,则推荐使用极小极大设计。

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