Statistical Methods and Adaptive Trial Designs for Cardiovascular Outcomes with Information Sharing

具有信息共享的心血管结果的统计方法和适应性试验设计

• 批准号: 10594472
• 负责人: Alexander Mark Kaizer
• 金额: $ 14.84万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594472
• 关键词: Address; Agreement; American; American Heart Association; Award; Biology; Biometry; Cardiology; Cardiovascular Physiology; Cardiovascular system; Clinical; Clinical Trials; Clinical Trials Design; Colorado; Complex; Computer software; Consumption; Coupled; Data; Data Set; Data Sources; Development; Diabetes Mellitus; Economics; Ethics; Evaluation Studies; Future; Goals; Informatics; Lipids; Lipoproteins; Medical; Medical center; Medicine; Mentored Research Scientist Development Award; Mentors; Mentorship; Metabolism; Methodology; Methods; Modeling; National Heart, Lung, and Blood Institute; Outcome; Outcomes Research; Participant; Phase; Probability; Process; Property; Protocols documentation; Research; Research Design; Research Personnel; Resource Allocation; Resources; Role; Sampling; Series; Software Tools; Source; Statistical Methods; System; Testing; Time; Training; Translating; Universities; Validation; Work; arm; design; experience; flexibility; improved; meetings; member; novel; novel therapeutics; precision medicine; premature; professor; prospective; research clinical testing; simulation; standard of care; treatment arm; treatment strategy; trial design; user friendly software; wasting

Project Summary/Abstract. This application for a K01 award describes the research & mentoring plans and coursework for Dr. Alexander Kaizer, an Assistant Professor of Biostatistics and Informatics at the University of Colorado-Anschutz Medical Campus (CU-AMC), to achieve advancement towards independent research in the use of adaptive designs for cardiovascular outcome clinical trials that facilitate information sharing across different sources of data to improve the statistical efficiency of evaluating new therapies. The process to develop effective novel treatments traditionally proceeds through a series of studies and phases. Conventionally, each phase is treated independently from previous phases, which are traditionally only used in the design stage of a new trial. This represents a potentially inefficient use of all available data that could be incorporated beyond the design stage and represents an important limitation for newer trial designs that may include multiple treatments within the context of a single protocol, but where comparisons only use concurrently collected data. The statistical methodologies and trial designs proposed in this application address this limitation by developing new methods to facilitate information sharing along with applications to platform trial designs. In this award, the development of statistical methods will be coupled with formal training in the biology of the cardiovascular system to assure that these new methods have seamless application in the design of cardiovascular outcomes research. To achieve the training goals and research aims laid out in this K01, a research team of three mentors has been assembled. Dr. John Kittelson, Professor of Biostatistics and Informatics at CU-AMC, is an expert in clinical trial design and has extensive experience with cardiovascular trial implementation and analysis. Dr. Gregory Schwartz, Professor of Medicine at CU-AMC and Chief of the Cardiology Section at the VA Medical Center, is a leader in proposing, implementing, and disseminating cardiovascular outcome clinical trials. Dr. Robert Eckel, Professor of Medicine at CU-AMC, past President of the American Heart Association, and President-elect of the American Diabetes Association, is an expert in lipid and lipoprotein metabolism and diabetes. In Aim 1, we will develop statistical methods for incorporating data from supplemental sources, such as past trials, into the analysis of a current study based on their exchangeability (i.e., equivalence) after adjusting for covariates (also known as information sharing). In Aim 2, we will develop adaptive platform trial designs that consider new treatment arms compared to a shared control arm. To improve the accessibility of the new methods, user-friendly software will be developed (Aim 3). The methods and designs from these aims will be evaluated via rigorous simulation study to understand their small sample properties under various scenarios. Methods will be illustrated through application to previously conducted cardiovascular trial data available from the NHLBI BioLINCC. Together, these methods for information sharing and adaptive trial designs will improve the efficiency of the research process and take fuller advantage of available information for statistical inference.

项目摘要/摘要。这份K01奖项申请书描述了研究和指导计划以及 亚历山大·凯泽博士的课程，他是该大学生物统计学和信息学的助理教授 科罗拉多-安舒茨医学院(CU-AMC)，以实现在 心血管结局临床试验中促进信息共享的适应性设计的使用 不同的数据来源，以提高评估新疗法的统计效率。要开发的过程 有效的新疗法传统上是通过一系列研究和阶段进行的。按照惯例，每个人 阶段独立于以前的阶段进行处理，这些阶段传统上仅在 新的审判。这可能会导致所有可用数据的使用效率低下，这些数据可能会被合并到 设计阶段，对于可能包括多种治疗的较新试验设计来说，这是一个重要限制 在单个协议的上下文中，但其中比较仅使用同时收集的数据。统计数字 本申请中提出的方法学和试验设计通过开发新方法解决了这一限制 以促进信息共享以及平台试验设计的应用程序。在这个奖项中，发展了 统计方法将与心血管系统生物学的正式培训相结合，以确保 这些新方法在心血管结局研究的设计中得到了无缝的应用。要实现 根据本次K01制定的培养目标和研究目标，组建了一个由三名导师组成的研究团队。 CU-AMC生物统计学和信息学教授John Kittelson博士是临床试验设计方面的专家 并在心血管试验实施和分析方面拥有丰富的经验。格雷戈里·施瓦茨博士， CU-AMC医学教授兼退伍军人医学中心心脏科科长，在 提出、实施和传播心血管结果临床试验。罗伯特·埃克尔教授 CU-AMC医学博士，美国心脏协会前主席，美国心脏协会当选主席 糖尿病协会，是脂和脂蛋白代谢和糖尿病方面的专家。 在目标1中，我们将开发统计方法，纳入补充来源的数据，例如过去的数据 试验，在调整后，基于它们的互换性(即，等价性)对当前研究进行分析 协变量(也称为信息共享)。在目标2中，我们将开发自适应平台试验设计 考虑将新的治疗臂与共享的控制臂进行比较。为了提高新方法的可及性， 将开发用户友好的软件(目标3)。从这些目标出发的方法和设计将被评估。 通过严格的模拟研究，了解它们在各种场景下的小样本特性。方法 将通过应用NHLBI提供的先前进行的心血管试验数据来说明 BioLINCC.总之，这些信息共享和适应性试验设计的方法将提高效率 并更充分地利用现有信息进行统计推断。