Optimal sample size division in two-stage seamless designs

被引:0
|
作者
Berry, Lindsay R. [1 ]
Marion, Joe [1 ]
Berry, Scott M. [1 ,2 ]
Viele, Kert [1 ,3 ]
机构
[1] Berry Consultants LLC, Austin, TX 78746 USA
[2] Univ Kansas, Med Ctr, Dept Biostat, Kansas City, KS USA
[3] Univ Kentucky, Dept Biostat, Lexington, KY USA
关键词
multiple testing; seamless phase 2/3 trial; treatment selection; II/III CLINICAL-TRIALS; SEQUENTIAL DESIGNS; HYPOTHESES SELECTION; INTERIM;
D O I
10.1002/pst.2394
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] A grid-search algorithm for optimal allocation of sample size in two-stage association studies
    S. H. Wen
    C. K. Hsiao
    [J]. Journal of Human Genetics, 2007, 52 : 650 - 658
  • [22] A grid-search algorithm for optimal allocation of sample size in two-stage association studies
    Wen, S. H.
    Hsiao, C. K.
    [J]. JOURNAL OF HUMAN GENETICS, 2007, 52 (08) : 650 - 658
  • [23] Two-stage k-sample designs for the ordered alternative problem
    Shan, Guogen
    Hutson, Alan D.
    Wilding, Gregory E.
    [J]. PHARMACEUTICAL STATISTICS, 2012, 11 (04) : 287 - 294
  • [24] Optimal sample sizes for group testing in two-stage sampling
    Antonio Montesinos-Lopez, Osval
    Eskridge, Kent
    Montesinos-Lopez, Abelardo
    Crossa, Jose
    [J]. SEED SCIENCE RESEARCH, 2015, 25 (01) : 12 - 28
  • [25] Optimal Training Sample Partitioning for Two-Stage Adaptive Detectors
    Abramovich, Yuri I.
    Johnson, Ben A.
    Spencer, Nicholas K.
    [J]. 2008 IEEE RADAR CONFERENCE, VOLS. 1-4, 2008, : 1005 - +
  • [26] Group sequential, sample size re-estimation and two-stage adaptive designs in clinical trials: A comparison
    Shih, WJ
    [J]. STATISTICS IN MEDICINE, 2006, 25 (06) : 933 - 941
  • [27] Two-stage sampling for etiologic studies - Sample size and power
    Schaubel, D
    Hanley, J
    Collet, JP
    Boivin, JF
    Sharpe, C
    Morrison, HI
    Mao, Y
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 1997, 146 (05) : 450 - 458
  • [28] Two-Stage Sample Size Reassessment Using Perturbed Unblinding
    Shih, Weichung Joe
    [J]. STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 2009, 1 (01): : 74 - 80
  • [29] Two-stage genomewide association: Power and sample size for replication
    Bull, S. B.
    Sun, L.
    Xie, X.
    Wui, L. Y.
    Paterson, A. D.
    [J]. GENETIC EPIDEMIOLOGY, 2007, 31 (05) : 464 - 464
  • [30] Optimal Two-Stage Designs to Evaluate a Series of New Agents or Treatments
    Mukhi, Vandana
    Shao, Yongzhao
    [J]. STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 2009, 1 (04): : 377 - 387