Statistical Methods for Clinical Studies

临床研究的统计方法

• 批准号: 7524890
• 负责人: Michael L. LeBlanc
• 金额: $ 24.95万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-03-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7524890
• 关键词: Algorithms; Antineoplastic Agents; Biochemical Genetics; Biological Markers; Characteristics; Clinical; Clinical Research; Clinical Trials; Clinical Trials Design; Complex; Computer software; Computing Methodologies; Data; Development; Diagnosis; Disease; Disease regression; Evaluation; Genetic; Heterogeneity; Incidence; Intervention; Joints; Knowledge; Malignant Neoplasms; Measures; Methods; Modeling; Molecular; Molecular Target; Outcome; Patients; Phase; Phase III Clinical Trials; Public Health; Research; Statistical Methods; Subgroup; Testing; Time; Treatment Efficacy; abstracting; base; cancer classification; cancer therapy; computerized data processing; design; improved; interest; mortality; new technology; outcome forecast

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.

描述（由申请人提供）：项目概要/摘要对癌症的遗传和生化机制的理解的增加导致了用于癌症的诊断、分类的新技术，并且现在导致了一系列可能对具有特定分子属性的癌症有效的治疗的开发。这些新的治疗方法为临床试验开发改进的设计和数据自适应分析方法提供了机会和必要性。具体而言，本研究将考虑以下内容：1）新靶向治疗的II期和III期研究。一些新的抗癌药物提供的临床益处随疾病的靶向表达而变化;因此，需要更好的设计以避免错过有希望的药物。策略将包括亚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。将评价为具有不同疾病和治疗疗效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连续测量的生物标志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，以估计生物标志物的影响，在存在潜在的时间依赖性混杂对患者的结果。还将安装软件，以便利使用作为本提案一部分而开发的方法。对降低癌症死亡率和发病率的新干预措施进行评估是公众非常关心的问题。在过去的几年中，在分子靶向治疗的开发和潜在生物标志物的鉴定方面取得了快速进展。至关重要的是，这些新的治疗方法和生物标志物必须以严格有效的方式进行评估，以最好地为患者服务，并扩大对这些复杂疾病的了解。公共卫生相关性：该提案的主要重点是开发适用于单独使用或与其他当前癌症疗法联合使用的靶向药物的设计和分析方法。我们将开发和评估灵活的临床试验设计的操作特征，这些设计将基于分子属性的生物异质性纳入其中。我们还将研究自适应统计算法，用于模拟患者结局，并确定可能从这些新治疗中获益最多的患者群体。