Sample Size Calculations for Crossover Thorough QT Studies: Satisfaction of Regulatory Threshold and Assay Sensitivity

被引:11
|
作者
Anand, Suraj P. [1 ]
Murray, Sharon C. [2 ]
Koch, Gary G. [3 ]
机构
[1] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
[2] GlaxoSmithKline Inc, Discovery Biometr, Oncol, Res Triangle Pk, NC USA
[3] Univ N Carolina, Dept Biostat, Chapel Hill, NC USA
关键词
Correlation structure; ICH E14; Monte Carlo simulation; Power and sample size; QTc prolongation; Thorough QT; QTc study; CLINICAL-TRIALS; QT/QTC; DESIGN; POWER;
D O I
10.1080/10543400903582000
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
The cost for conducting a othorough QT/QTc studyo is substantial and an unsuccessful outcome of the study can be detrimental to the safety profile of the drug, so sample size calculations play a very important role in ensuring adequate power for a thorough QT study. Current literature offers some help in designing such studies, but these methods have limitations and mostly apply only in the context of linear mixed models with compound symmetry covariance structure. It is not evident that such models can satisfactorily be employed to represent all kinds of QTc data, and the existing literature inadequately addresses whether there is a change in sample size and power for more general covariance structures for the linear mixed models. We assess the use of some of the existing methods to design a thorough QT study through data arising from a GlaxoSmithKline (GSK)-conducted thorough QT study, and explore newer models for sample size calculation. We also provide a new method to calculate the sample size required to detect assay sensitivity with adequate power.
引用
收藏
页码:563 / 579
页数:17
相关论文
共 50 条
  • [31] SAMPLE-SIZE CALCULATIONS FOR CLINICAL-PHARMACOLOGY STUDIES
    STOLLEY, PD
    STROM, BL
    CLINICAL PHARMACOLOGY & THERAPEUTICS, 1986, 39 (05) : 489 - 490
  • [32] Sample-size calculations for studies with correlated ordinal outcomes
    Kim, HY
    Williamson, JM
    Lyles, CM
    STATISTICS IN MEDICINE, 2005, 24 (19) : 2977 - 2987
  • [33] POWER AND SAMPLE SIZE CALCULATIONS FOR MENDELIAN RANDOMIZATION STUDIES.
    Freeman, Guy
    Cowling, Benjamin
    Schooling, Mary
    AMERICAN JOURNAL OF EPIDEMIOLOGY, 2013, 177 : S117 - S117
  • [34] On sample size for sensitivity and specificity in prospective diagnostic accuracy studies
    Li, JL
    Fine, J
    STATISTICS IN MEDICINE, 2004, 23 (16) : 2537 - 2550
  • [35] False discovery rate, sensitivity and sample size for microarray studies
    Pawitan, Y
    Michiels, S
    Koscielny, S
    Gusnanto, A
    Ploner, A
    BIOINFORMATICS, 2005, 21 (13) : 3017 - 3024
  • [36] Sample size calculation for bioequivalence studies with high-order crossover designs
    Qu, RP
    Zheng, HJ
    CONTROLLED CLINICAL TRIALS, 2003, 24 (04): : 436 - 439
  • [37] Bayesian sample size calculations for comparing two strategies in SMART studies
    Turchetta, Armando
    Moodie, Erica E. M.
    Stephens, David A.
    Lambert, Sylvie D.
    BIOMETRICS, 2023, 79 (03) : 2489 - 2502
  • [38] Sample size calculations for studies designed to evaluate diagnostic test accuracy
    Branscum, Adam J.
    Johnson, Wesley O.
    Gardner, Ian A.
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2007, 12 (01) : 112 - 127
  • [39] Sample size calculations for clinical studies allowing for uncertainty about the variance
    Julious, Steven A.
    Owen, Roger J.
    PHARMACEUTICAL STATISTICS, 2006, 5 (01) : 29 - 37
  • [40] Sample size calculations for ROC studies: parametric robustness and Bayesian nonparametrics
    Cheng, Dunlei
    Branscum, Adam J.
    Johnson, Wesley O.
    STATISTICS IN MEDICINE, 2012, 31 (02) : 131 - 142