Statistical Methods and Software for More Efficient, Ethical, and Affordable Clin

统计方法和软件使临床更加高效、合乎道德且经济实惠

• 批准号: 8233680
• 负责人: Bradley P Carlin
• 金额: $ 34.46万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8233680
• 关键词: Address; Advisory Committees; Alcohols; Area; Bayesian Method; Behavior; Berry; Biometry; Books; Cancer Center; Characteristics; Clinic; Clinical; Clinical Investigator; Clinical Trials; Clinical Trials Design; Collection; Computer software; Conflict (Psychology); Control Groups; Cost Analysis; Data; Data Set; Data Sources; Databases; Development; Devices; Doctor of Medicine; Drops; Enrollment; Ethics; Event; Expert Opinion; Florida; Goals; Inferior; Institution; Institutional Review Boards; Intervention Studies; Investigational Drugs; Knowledge; Language; Lead; Letters; Malignant Neoplasms; Methodology; Methods; Minnesota; Modeling; Monitor; National Cancer Institute; Nature; New Device Approvals; New Drug Approvals; Patients; Pharmaceutical Preparations; Phase; Phase I/II Trial; Process; Protocols documentation; Public Health; Publishing; Radiologic Health; Randomized; Reporting; Research Personnel; Review Committee; Sample Size; Software Tools; Source; Statistical Methods; Statistical Models; Text; Time; Universities; Ursidae Family; Work; base; computer code; cost; design; hazard; improved; member; novel; simulation; software repository; theories; user friendly software; user-friendly; web page; web site

DESCRIPTION (provided by applicant): Clinical trials are becoming increasingly expensive, and patients are more and more reluctant to enroll and to persist with the protocol, further increasing costs. We propose to develop Bayesian statistical methods and software for more efficient, ethical, and affordable clinical trials. These methods incorporate all sources of knowledge (structural constraints, expert opinion, and both historical and experimental data), thus reducing sample size and typically leading to increases in statistical power and reductions in cost and ethical hazard, since fewer patients need be exposed to the inferior treatment. The methods are also better able to adapt to unanticipated changes that inevitably arise as the trial progresses. In Aim 1 we will develop novel methods to exploit available historical data. In Aim 2 we look at the more general problem of combining inference across related subpopulations, when the subpopulations are not exchangeable. Here our approach is based on random clustering of subpopulations. Finally, Aim 3 is concerned with the development of user-friendly computer code that can be publicly distributed. The end result will be clinical trials that come to conclusions more quickly with fewer patients randomized to inferior treatments. Our methods and software should have immediate impact on practice not only for early phase studies, but for large confirmatory trials as well. In the longer term, improvements in trial compliance and accrual should also obtain. PUBLIC HEALTH RELEVANCE: The relevance of this work to public health lies in its ability to randomize fewer patients to control groups in clinical trials, since reliable historical information on such groups will be brought to bear. Patients are thus treated more ethically, and trial conclusions are obtained more quickly. The work has a potentially large impact on small studies of interventions in cancer, alcohol, and many other areas where sample sizes are small yet other sources of information exist that can accelerate the process while maintaining scientific integrity.

描述（由申请人提供）：临床试验变得越来越昂贵，患者越来越不愿意入学并坚持使用该协议，进一步提高了成本。我们建议开发贝叶斯统计方法和软件，以进行更有效，道德和负担得起的临床试验。这些方法结合了所有知识来源（结构性约束，专家意见以及历史和实验数据），从而减少了样本量，并且通常导致统计能力和成本和道德危害的降低，因为较少的患者需要暴露于次要治疗中。这些方法还可以更好地适应意外的变化，随着试验的进行，不可避免地会出现。在AIM 1中，我们将开发新的方法来利用可用的历史数据。在AIM 2中，我们探讨了在亚种群不可交换时将推理组合到相关亚群中的更普遍的问题。在这里，我们的方法基于亚群的随机聚类。最后，AIM 3关注的是可以公开分发的用户友好计算机代码的开发。最终结果将是临床试验，这些试验得出的结论更快，而随机分配到下等治疗的患者较少。我们的方法和软件不仅对早期研究，而且对大型验证试验也应立即影响实践。从长远来看，还应提高试验合规性和应计的改进。 公共卫生相关性：这项工作与公共卫生的相关性在于其能够将较少的患者随机以对照临床试验的对照组进行对照组，因为将带来有关此类群体的可靠历史信息。因此，对患者进行更有道德的治疗，并更快地得出试验结论。这项工作对癌症，酒精中的干预措施以及样本量较小的许多其他领域的干预措施的小型研究具有很大的影响，而其他信息来源也可以加速该过程，同时维持科学完整性。