Use Bayesian methods to facilitate the data integration for complex clinical trials

使用贝叶斯方法促进复杂临床试验的数据集成

• 批准号: 10714225
• 负责人: Yong Zang
• 金额: $ 32.02万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714225
• 关键词: 3-Dimensional; Acceleration; Address; Advocate; Algorithms; Back; Bayesian Method; Bayesian Modeling; Bayesian Network; Benefits and Risks; Big Data; Biological Response Modifier Therapy; Biomedical Technology; Calibration; Clinical; Clinical Data; Clinical Trials; Complex; Computer software; Data; Data Analyses; Development; Dimensions; Dose; Ethics; Goals; Guidelines; Health; Individual; Inferior; Likelihood Functions; Meta-Analysis; Methodology; Methods; Modeling; Monitor; Names; Nature; Online Systems; Organoids; Outcome; Patients; Performance; Phase; Phase I and II Vaccine Trials; Physicians; Population; Prediction of Response to Therapy; Process; Protocols documentation; Randomized; Research; Research Proposals; Resources; Rewards; Scheme; Science; Selection for Treatments; Series; Speed; Statistical Models; Subgroup; Surrogate Markers; Survival Rate; Testing; Therapeutic Effect; Time; Tissues; Toxic effect; Update; arm; biomarker evaluation; clinical practice; data integration; drug discovery; flexibility; graphical user interface; heterogenous data; immunotherapy trials; improved; novel; oncology trial; optimal treatments; participant enrollment; patient safety; patient subsets; personalized medicine; response; sound; stem cells; tool; treatment arm; treatment effect; trial design; tumor growth; user-friendly; web app

Project Summary/Abstract The primary goal of this research proposal is to develop general and efficient Bayesian statistical methods to enhance drug discovery using complex clinical trial data. Rapid development in biomedical sciences is generat- ing increasingly large and heterogeneous health-related data, including toxicity and efficacy endpoints, long-term survival time, and surrogate biomarker profile. Although the data are heterogeneous by nature, they serve the same central drug discovery question and multiple types of outcomes may be collected from the same individ- ual. Therefore, a successful information integration of these “big data” generated during different periods of complex clinical trials can improve the power of the hypothesis testing, speed the drug discovery process, and enhance the individual ethics of the trials, among other benefits. However, significant efforts are needed to mit- igate the gaps of the data generated from different platforms; otherwise, the accumulated inconsistencies and biases may distort the statistical inference for complex clinical trials. We will tackle this important and challenging research topic by developing a series of novel Bayesian statistical methods. In particular, we will (1) develop a jointly modeling approach using the patient-derived organoids (PDO) and the paired clinical outcome to select and verify personalized medicine (2) construct a Bayesian subgroup-specific dose optimization model to synthe- size risk-benefit evidence across multi-dimensional heterogeneous data and (3) develop a Bayesian calibrated network meta-analysis method to integrate the control information of master protocol trials during different ran- domization stages. In addition, we will develop user-friendly web apps to facilitate the widespread application of the proposed methods in clinical practice. All the aims in this proposal are driven by practical issues from complex clinical trials. The proposed research are general and encompasses a variety of clinical trial settings, including oncology and vaccine trials, phase I, II, and III trials, standard and master protocol trials, long-term and short-term outcomes, and surrogate marker. The preliminary results show that the proposed methods can substantially reduce the bias of the data and yield highly efficient and reliable performances, compared with other existing methods.

项目概要/摘要 本研究提案的主要目标是开发通用且有效的贝叶斯统计方法 利用复杂的临床试验数据增强药物发现生物医学科学正在快速发展。 收集日益庞大且异质的健康相关数据，包括毒性和疗效终点、长期 生存时间和替代生物标志物概况虽然数据本质上是异质的，但它们服务于 可以从同一个人收集相同的中心药物发现问题和多种类型的结果 因此，对这些不同时期产生的“大数据”进行了成功的信息整合。 复杂的临床试验可以提高假设检验的能力，加快药物发现过程，并且 然而，除了其他好处外，还需要做出重大努力来减轻影响。 消除不同平台生成的数据之间的差距；否则，会产生累积的不一致和 偏差可能会扭曲复杂临床试验的统计推断，我们将解决这一重要且具有挑战性的问题。 通过开发一系列新颖的贝叶斯统计方法来研究主题，特别是，我们将（1）开发一种。 使用患者来源的类器官（PDO）和配对的临床结果联合建模方法来选择 并验证个性化医疗（2）构建贝叶斯亚组特定剂量优化模型来综合- 跨多维异构数据确定风险收益证据的大小，并 (3) 开发贝叶斯校准模型 网络元分析方法来整合不同运行期间主协议试验的控制信息 此外，我们将开发用户友好的网络应用程序以促进广泛应用。 该提案中的所有目标都是由实际问题驱动的。 复杂的临床试验。拟议的研究是一般性的，涵盖各种临床试验环境， 包括肿瘤学和疫苗试验、I、II 和 III 期试验、标准和主方案试验、长期试验 和短期结果以及替代标记初步结果表明所提出的方法可以。 与其他方法相比，大大减少了数据的偏差，并产生高效可靠的性能 现有的方法。