A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies.
A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies.
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作者：
Li J;Zhao L;Tian L;Cai T;Claggett B;Callegaro A;Dizier B;Spiessens B;Ulloa-Montoya F;Wei LJ
为了通过随机化、比较性临床研究或一系列试验评价新疗法与对照，由于研究患者人群的异质性，可实施预先规定的预测性富集程序以鉴定“可富集”亚群。对于该亚群中的患者，预期该治疗具有理想的总体风险-获益特征。为了开发和验证这种“治疗-诊断协同开发”策略，可以使用来自一系列类似研究或单个试验的三个独立数据集进行三步程序。在第一阶段，我们基于患者的基线信息创建各种候选评分系统，例如，使用第一数据集的参数模型。每个个体评分反映了具有相似基线特征的未来患者的预期平均治疗差异。大的分数表明这些患者倾向于从新疗法中获益。在第二步，使用来自这些评分系统的全部证据来识别潜在有希望的、可富集的亚组。在最后阶段，我们通过双样本推理程序验证这样的选择，以评估统计和临床上的治疗效果与第三个数据集，所谓的holdout样本。当研究规模不大时，可以使用“交叉训练-评估”过程将前两个步骤联合收割机结合起来。进行了全面的数值研究，调查所提出的方法的操作特性。整个富集过程与心血管试验的数据来说明，以评估β受体阻滞剂与安慰剂治疗慢性心力衰竭患者。
To evaluate a new therapy versus a control via a randomized, comparative clinical study or a series of trials, due to heterogeneity of the study patient population, a pre-specified, predictive enrichment procedure may be implemented to identify an “enrichable” subpopulation. For patients in this subpopulation, the therapy is expected to have a desirable overall risk-benefit profile. To develop and validate such a “therapy-diagnostic co-development” strategy, a three-step procedure may be conducted with three independent data sets from a series of similar studies or a single trial. At the first stage, we create various candidate scoring systems based on the baseline information of the patients via, for example, parametric models using the first data set. Each individual score reflects an anticipated average treatment difference for future patients who share similar baseline profiles. A large score indicates that these patients tend to benefit from the new therapy. At the second step, a potentially promising, enrichable subgroup is identified using the totality of evidence from these scoring systems. At the final stage, we validate such a selection via two-sample inference procedures for assessing the treatment effectiveness statistically and clinically with the third data set, the so-called holdout sample. When the study size is not large, one may combine the first two steps using a “cross-training-evaluation” process. Comprehensive numerical studies are conducted to investigate the operational characteristics of the proposed method. The entire enrichment procedure is illustrated with the data from a cardiovascular trial to evaluate a beta-blocker versus a placebo for treating chronic heart failure patients.