Statistical Methods for Clinical Studies

描述(由申请人提供)：项目摘要/摘要对癌症的遗传和生化机制的了解的加深导致了诊断、癌症分类的新技术，现在又开发了一系列可能对具有特定分子属性的癌症有效的治疗方法。这些新的治疗方法为临床试验开发改进的设计和数据适应性分析方法提供了机会和必要性。具体地说，这项研究将考虑以下内容：1)新靶向治疗的第二阶段和第三阶段研究。一些新的抗癌药物提供的临床益处因疾病的靶向表达而有所不同；因此，需要更好的设计来避免错过有希望的药物。战略将包括分组和收缩方法的联合测试。2)探索患者结局的自适应回归方法。涉及靶向治疗的新研究结果的复杂性要求更好地理解遗传属性和治疗效果之间的关系。将评估为具有不同预后和治疗效果的患者亚组构建规则的计算方法。3)纵向标记过程数据。还需要改进的方法来了解顺序测量的生物标记物的关联及其对治疗的影响和相互作用。我们将考虑因果建模结构来评估生物标记物在潜在的时间依赖的混杂对患者结果的影响。还将安装软件，以便利使用作为本提案的一部分制定的方法。对降低癌症死亡率和发病率的新干预措施的评估具有重大的公众利益。在过去的几年里，在分子靶向治疗的发展和潜在生物标记物的识别方面取得了快速的进展。以严格和有效的方式评估这些新的治疗方法和生物标志物，以便最好地为患者服务，并扩大对这些复杂疾病的了解，这一点至关重要。公共卫生相关性：这项提案的主要焦点是开发适合于单独使用或与其他当前癌症治疗方法联合使用的靶向药物的设计和分析方法。我们将开发和评估纳入基于分子属性的生物异质性的灵活临床试验设计的操作特征。我们还将研究自适应统计算法，以模拟患者的结果，并确定可能从这些新治疗中受益最大的患者组。

DESCRIPTION (provided by applicant): PROJECT SUMMARY/ABSTRACT Increased understanding of the genetic and biochemical mechanisms of cancer has led to new technologies for diagnosis, classification of cancers and now to the development of an array of treatments that may have efficacy for cancers with specific molecular attributes. These new treatments provide both the opportunity and necessity to develop improved designs and data adaptive analysis methods for clinical trials. Specifically, this research will consider the following: 1) Phase II and Phase III studies for new targeted treatments. Some new anticancer agents offer clinical benefits that vary with respect to target expression of the disease; therefore, better designs are needed to avoid missing promising agents. Strategies will include joint testing of subgroups and shrinkage methods. 2) Adaptive regression methods for exploring patient outcome. The complexity of results from new studies involving targeted therapy demands a better understanding of the relationships between genetic attributes and treatment efficacy. Computational methods that construct rules for patient subgroups with differing prognoses and treatment efficacy will be evaluated. 3) Longitudinal marker process data. Improved methods are also needed to understand the association of sequentially measured biomarkers and their impact and interactions with respect to treatment. We will consider causal modeling constructs to estimate effects of biomarkers in the presence of potentially time-dependant confounding on patient outcome. Software will also be implemented to facilitate the use of methods developed as part of this proposal. The evaluation of new interventions to reduce mortality and incidence of cancers is of significant public interest. Over the last few years there has been rapid progress in the development of molecular targeted therapies and in the identification of potential biomarkers. It is crucial that these new treatments and biomarkers be evaluated in a rigorous and efficient manner to best serve patients and to expand knowledge of these complex diseases. PUBLIC HEALTH RELEVANCE: The major focus of this proposal is the development of design and analysis methods appropriate for targeted agents used alone or in combination with other current cancer therapies. We will develop and evaluate the operating characteristics of flexible clinical trial designs which incorporate biologic heterogeneity based on molecular attributes. We will also study adaptive statistical algorithms for modeling patient outcome and for identifying of groups of patients who may benefit most from these new treatments.