Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD 20993, USA.
Clin Trials. 2010 Oct;7(5):525-36. doi: 10.1177/1740774510375455. Epub 2010 Jul 1.
The current practice for seeking genomically favorable patients in randomized controlled clinical trials using genomic convenience samples.
To discuss the extent of imbalance, confounding, bias, design efficiency loss, type I error, and type II error that can occur in the evaluation of the convenience samples, particularly when they are small samples. To articulate statistical considerations for a reasonable sample size to minimize the chance of imbalance, and, to highlight the importance of replicating the subgroup finding in independent studies.
Four case examples reflecting recent regulatory experiences are used to underscore the problems with convenience samples. Probability of imbalance for a pre-specified subgroup is provided to elucidate sample size needed to minimize the chance of imbalance. We use an example drug development to highlight the level of scientific rigor needed, with evidence replicated for a pre-specified subgroup claim.
The convenience samples evaluated ranged from 18% to 38% of the intent-to-treat samples with sample size ranging from 100 to 5000 patients per arm. The baseline imbalance can occur with probability higher than 25%. Mild to moderate multiple confounders yielding the same directional bias in favor of the treated group can make treatment group incomparable at baseline and result in a false positive conclusion that there is a treatment difference. Conversely, if the same directional bias favors the placebo group or there is loss in design efficiency, the type II error can increase substantially.
Pre-specification of a genomic subgroup hypothesis is useful only for some degree of type I error control.
Complete ascertainment of genomic samples in a randomized controlled trial should be the first step to explore if a favorable genomic patient subgroup suggests a treatment effect when there is no clear prior knowledge and understanding about how the mechanism of a drug target affects the clinical outcome of interest. When stratified randomization based on genomic biomarker status cannot be implemented in designing a pharmacogenomics confirmatory clinical trial, if there is one genomic biomarker prognostic for clinical response, as a general rule of thumb, a sample size of at least 100 patients may be needed to be considered for the lower prevalence genomic subgroup to minimize the chance of an imbalance of 20% or more difference in the prevalence of the genomic marker. The sample size may need to be at least 150, 350, and 1350, respectively, if an imbalance of 15%, 10% and 5% difference is of concern.
目前在随机对照临床试验中使用基因组便利样本来寻找具有有利基因组的患者的做法。
讨论在评估便利样本时可能出现的不平衡、混杂、偏差、设计效率损失、I 型错误和 II 型错误的程度，特别是当样本较小时。阐述合理样本量的统计考虑因素，以最小化不平衡的可能性，并强调在独立研究中复制亚组发现的重要性。
使用四个反映最近监管经验的案例示例来强调便利样本存在的问题。提供了预定亚组的不平衡概率，以阐明最小化不平衡可能性所需的样本量。我们使用一个药物开发示例来突出具有预定亚组声称的证据所需的科学严谨性水平。
评估的便利样本范围从意向治疗样本的 18%到 38%，每个臂的样本量从 100 到 5000 名患者不等。基线不平衡的发生概率可能高于 25%。轻度至中度多重混杂因素会导致对治疗组有利的相同方向偏差，从而使治疗组在基线时无法比较，并得出存在治疗差异的假阳性结论。相反，如果相同方向的偏差有利于安慰剂组或设计效率损失，则 II 型错误会大幅增加。
基因组亚组假设的预先指定仅对一定程度的 I 型错误控制有用。
在随机对照试验中应首先完全确定基因组样本的确定，以探索是否有利的基因组患者亚组在没有明确的先前知识和理解如何影响药物靶标机制对感兴趣的临床结果时，提示治疗效果。当基于基因组生物标志物状态的分层随机化不能用于设计验证性药物基因组学临床试验时，如果存在一个对临床反应具有预测性的基因组生物标志物，则作为一般经验法则，至少需要 100 名患者的样本量，以考虑基因组生物标志物的较低流行率亚组，以最小化 20%或更多差异的不平衡的可能性。如果关注的是 15%、10%和 5%的差异，则样本量可能分别至少需要 150、350 和 1350。
