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Comparison of Bayesian and Frequentist Methods in Cardiovascular Clinical Trials

贝叶斯方法和频率方法在心血管临床试验中的比较

基本信息

批准号：
8034647
负责人：
Alice Rogot Pressman
金额：
$ 93.73万
依托单位：
KAISER FOUNDATION RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-22 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8034647
关键词：
Bayesian Analysis Bayesian Method Cardiovascular system Characteristics Clinical Clinical Research Clinical Trials Complex Conduct Clinical Trials Data Data Analyses Development Devices Diagnostic tests Effectiveness Ethics Futility Future Goals Gold HIV Health Inferior Length Life Light Malignant Neoplasms Measures Medicine Methodology Methods Outcome Patients Performance Probability Randomized Controlled Clinical Trials Recommendation Relative (related person)Reliance Research Research Personnel Resources Sampling Science Series Side Simulate Structure Techniques Technology Testing Time Variant Writing analytical method arm clinically relevant comparative comparative effectiveness computer program cost design human subject innovation programs public health relevance research in practice research study simulation trial comparing

项目摘要

DESCRIPTION (provided by applicant): Bayesian clinical trials (BCT) are purported to be more efficient in terms of cost and human subjects than existing frequentist methods. At this time, BCTs have been implemented for cancer trials, device trials, and some HIV trials, but are the rare exception in other fields of health research. While traditional frequentist methods are widely understood and accepted in practice, Bayesian methods are not yet fully vetted, and, due to their reliance on heavy computations, have not until recently, been readily accessible in the typical clinical trial setting. This comparative effectiveness study aims to consider applications of Bayesian methodology for the clinical trial in comparison to other analytic alternatives using real-life data. This application proposes a series of side-by-side comparisons between existing frequentist group sequential methods, and newer Bayesian methods of analysis of clinical trials using data from completed cardiovascular trials. The trials vary by size, outcome, impact on their field, and date of original analysis. Investigators will re- analyze the data from each trial using two typical frequentist group sequential plans (Obrien-Fleming and Lan- DeMets alpha spending functions) and several variations of Bayesian analysis with informative, non- informative, and skeptical prior distributions in order to determine the sensitivity of outcome and trial duration to analytic assumptions for both standard group sequential methods and BCTs. At each analysis time point, calculations will be made to determine the probability that there is a clinically relevant effect and probability of futility. Bootstrap sampling with a simulated Null distribution will be used to estimate experiment-wise error rates for each analysis. In addition, at each analysis point over time, conditional power to detect an effect will be calculated. In this way direct comparisons can be made as to the relative effectiveness of the different methods of analysis. Finally, results from all trials will be synthesized to identify the characteristics of a cardiovascular clinical trial for which BCT methods would be more or less advantageous. The results from this study will provide critical information to further the development of analytic techniques for clinical trials and to assess the relative efficiency of Bayesian methods using the innovative approach of real- life data rather than the standard use of simulated trial data. PUBLIC HEALTH RELEVANCE: Traditional frequentist methods for analysis of clinical trials are generally accepted as the gold standard in research practice. Newer Bayesian methods, however, hold great promise for a more efficient analytic approach, thereby lowering the costs of clinical research and enhancing the ethical construct of clinical trials by reducing the length of exposure time to inferior treatment arms. The results of this study will provide valuable information about the true comparative performance of the two methods and help identify the circumstances for which Bayesian approaches may provide advantages over conventional analytic techniques.

描述(由申请人提供)：贝叶斯临床试验(BCT)被认为在成本和人类受试者方面比现有的频率法更有效。目前，BCT已被用于癌症试验、设备试验和一些艾滋病毒试验，但在其他健康研究领域是罕见的例外。虽然传统的频域方法在实践中得到了广泛的理解和接受，但贝叶斯方法还没有得到充分的审查，并且由于它们依赖于繁重的计算，直到最近才在典型的临床试验环境中容易获得。这项比较有效性研究的目的是考虑贝叶斯方法在临床试验中的应用，并与使用真实数据的其他分析方案进行比较。这项申请提出了一系列并列比较现有的频率组序贯方法，以及使用已完成的心血管试验数据进行临床试验分析的较新的贝叶斯方法。这些试验的规模、结果、对其领域的影响以及最初分析的日期各不相同。调查人员将使用两个典型的频数组序贯计划(Obrien-Fleming和Lan-Demets Alpha支出函数)以及贝叶斯分析的几种变体，使用信息性、非信息性和怀疑性的先验分布重新分析每个试验的数据，以确定标准组序贯方法和BCT的结果和试验持续时间对分析假设的敏感性。在每个分析时间点，将进行计算以确定存在临床相关效应的概率和无效的概率。模拟零分布的Bootstrap抽样将用于估计每个分析的实验误差率。此外，随着时间的推移，在每个分析点，将计算检测效果的条件功率。这样，就可以直接比较不同分析方法的相对有效性。最后，将综合所有试验的结果，以确定BCT方法或多或少具有优势的心血管临床试验的特点。这项研究的结果将为进一步开发临床试验的分析技术提供关键信息，并使用真实数据的创新方法而不是模拟试验数据的标准使用来评估贝叶斯方法的相对效率。公共卫生相关性：临床试验分析的传统频率法被普遍认为是研究实践中的黄金标准。然而，较新的贝叶斯方法为更有效的分析方法带来了巨大的希望，从而降低了临床研究的成本，并通过减少接触劣质治疗武器的时间来增强临床试验的伦理构建。这项研究的结果将提供关于这两种方法的真实比较性能的有价值的信息，并有助于确定在什么情况下贝叶斯方法可能比传统分析技术更具优势。