Two-stage winner designs for non-inferiority trials with pre-specified non-inferiority margin

被引:2
|
作者
Wang, Pin-Wen [1 ]
Lu, Shou-En [2 ,3 ]
Lin, Yong [2 ,3 ]
Shih, Weichung J. [2 ,3 ]
Lan, K. K. Gordon [4 ]
机构
[1] Genentech Inc, Contract Via Experis,1 Dna Way, San Francisco, CA 94080 USA
[2] Rutgers State Univ, Sch Publ Hlth, Dept Biostat, 683 Hoes Lane West, Piscataway, NJ 08854 USA
[3] Rutgers State Univ, Rutgers Canc Inst New Jersey, Div Biometr, 120 Albany St, New Brunswick, NJ 08901 USA
[4] Johnson & Johnson, 920 Route 202, Raritan, NJ 08869 USA
关键词
Non-inferiority trials; Adaptive design; Two-stage design; TREATMENT SELECTION; CLINICAL-TRIALS; SEQUENTIAL DESIGNS; MACULAR DEGENERATION; RANIBIZUMAB;
D O I
10.1016/j.jspi.2016.10.001
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In drug development, a two-stage winner design (Lan et al. 2005, Shun et al. 2008) can be cost-effective when the best treatment is to be determined from multiple experimental treatments in superiority trials. However, the statistical methods assessing non-inferiority in a two-stage winner design have not yet been studied, for which the complexity arises in determining the critical value when parameter space is not a single point under the null hypothesis. Because the maximum error may not occur at the vertex of the null space, it is unclear if naive use of critical values and distributional results from the test for superiority remains correct. In this paper, we provided rigorous justifications to determine the critical value for testing non-inferiority hypothesis, with a pre-specified non-inferiority margin, in a two-stage winner design with two experimental treatments and an active control. We studied the distribution of the test statistics, critical values, sample size and power calculations using the exact distribution of the test statistics as well, as using normal approximations. Theoretical justifications and extensive numerical assessments were conducted to calculate the design parameters and evaluate the performance of our methods. (C) 2016 Elsevier B.V. All rights reserved.
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页码:44 / 61
页数:18
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