Statistical Software for Adaptive Oncology Clinical Trials

描述（由申请人提供）：最近的两项科学发展，一项是生物统计学，另一项是药物基因组学，可能对III期和无缝II/III期肿瘤试验的设计和监测产生重大影响，大大提高其成功的机会。人类基因组研究的进展表明，许多常见突变对于确定可能从分子靶向药物中获益的患者具有预后和预测价值。与此同时，生物统计学研究界对适应性临床试验设计的兴趣激增。适应性试验是指从试验本身获得的早期数据可以用来修改试验的未来进程，而不会破坏其完整性或统计有效性（Gallo等人，2006a, 2006b）。适应性设计在临床药物开发的早期和后期阶段都发挥着作用。然而，我们的兴趣是后期验证性试验（后期II期和III期），其目标是提高监管部门批准新药物的机会。即使在这个后期阶段，化合物的总体失败率为45%，而对于肿瘤试验，失败率几乎为60% （Kola和Landis， 2004年）。值得注意的是，到这个时候，发现和开发一种药物的很大一部分费用已经发生了。在造成这种流失的众多原因中，一个主要原因是选择了错误的人群来测试药物。越来越明显的是，不同基因组患者亚群之间的治疗效果可能存在很大差异。我们希望在肿瘤学中推广一种新型的验证性试验设计。其中，我们利用预测标记物可以识别对不同治疗剂敏感的患者这一事实，因此，与阴性标记物相比，阳性标记物的患者可能从靶向治疗中获益不同。预测标记提供了进行所谓的种群富集设计的机会（Temple, 2005）。基因组技术如微阵列和单核苷酸多态性基因分型可用于在试验筛选阶段确定患者的标记状态。如果一个标记物被认为可以预测测试药物，原则上可以将入组限制在携带有利基因型的患者亚群中，从而丰富研究人群并增加试验成功的机会。我们的目标是开发支持这些类型设计的统计软件。该软件将利用两阶段自适应设计的概念，如果生物标志物具有预测性，则第一阶段的结果可用于丰富第二阶段的种群。

DESCRIPTION (provided by applicant): Two recent scientific developments, one in biostatistics and one in pharmacogenomics are likely to have a major impact on the design and monitoring of phase III and seamless phase II/III oncology trials, greatly improving their chances of success. Advances in human genomic studies have shown that many common mutations have prognostic and predictive value for identifying patients who are likely to benefit from a molecularly targeted agent. At the same time there has been a surge of interest within the biostatistics research community in the design of adaptive clinical trials. An adaptive trial is one in which early data obtained from the trial itself can be used to modify the future course of the trial, without undermining its integrity or statistical validity (Gallo et. al., 2006a, 2006b). Adaptive designs play a role in both early and late stages of clinical drug development. Our interest, however, is in late stage confirmatory trials (late phase II and phase III), where the goal is to improve the chances for regulatory approval of a new medical compound. The overall failure rate of compounds even at this late stage is 45\%, and for oncology trials the failure rate is almost 60\% (Kola and Landis, 2004). It is worth noting that by this time significant proportions of the costs of discovering and developing a drug have been incurred. Among the many causes for this attrition, a major one is choosing the wrong population for the test drug. It is becoming increasingly apparent that treatment effects can differ greatly between different genomic patient subsets. We wish to promote a new type of design for confirmatory trials in oncology.in which we use the fact that predictive markers can identify patients who are sensitive to distinct therapeutic agents, such that patients with a positive marker might benefit differentially from the targeted therapy compared to patients with a negative marker. Predictive markers provide the opportunity to conduct so called population enrichment designs (Temple, 2005). Genomic technologies such as microarrays and single nucleotide polymorphism genotyping may be used to identify the marker status of patients during the screening phase of a trial. If a marker is considered predictive for the test drug one could in principle restrict enrollement to the subset of patients carrying the favorable genotype, thereby enriching the study population and increasing the chance of a successful trial. Our goal is to develop statistical software that will support these types of designs. The software will utilize the concept of two-stage adaptive designs in which the results at the first stage may be used to enrich the population at the second stage if the biomarker is predictive. PUBLIC HEALTH RELEVANCE: This project will support the development of new research methods and software for oncology trials in which predictive biomarkers may be used to enrich the population as the second stage of the design based on results observed at the first stage.