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.

项目摘要/摘要 该研究建议的主要目标是将一般和有效的贝叶斯统计方法开发为 使用复杂的临床试验数据增强药物发现。生物医学科学的快速发展是生成器 - 越来越大且异构健康相关的数据，包括毒性和效果终点，长期 生存时间和替代生物标志物。尽管数据本质上是异质的，但它们为 可以从相同的个体中收集相同的中央药物发现问题和多种类型的结果 - ual。因此，在不同时期生成的这些“大数据”的成功信息集成 复杂的临床试验可以提高假设检验的功能，加快药物发现过程和 增强试验的个人伦理，以及其他好处。但是，需要重大努力来缓解 从不同平台生成的数据的差距；否则，累积的不一致和 偏见可能会扭曲复杂临床试验的统计推论。我们将解决这个重要和挑战 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）发展 使用患者衍生的类器官（PDO）和配对的临床结果共同建模方法 并验证个性化医学（2）构建贝叶斯亚组剂量优化模型以融合 跨多维异质数据的尺寸风险融合证据，（3）开发贝叶斯校准 网络荟萃分析方法是在不同的ran-ran中整合总体协议试验的控制信息 剂量阶段。此外，我们将开发用户友好的Web应用程序，以促进宽度应用程序 临床实践中提出的方法。该提案中的所有目的都是由 复杂的临床试验。拟议的研究是一般的，包括各种临床试验环境， 包括肿瘤学和疫苗试验，第一阶段，II和III试验，标准和总体协议试验，长期 和短期结局和替代标记。初步结果表明，所提出的方法可以 与其他 现有方法。