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

描述(申请人提供)：临床试验变得越来越昂贵，患者越来越不愿意登记并坚持该方案，进一步增加了成本。我们建议开发贝叶斯统计方法和软件，以实现更有效、更道德、更负担得起的临床试验。这些方法结合了所有的知识来源(结构约束、专家意见以及历史和实验数据)，从而减少了样本量，通常会增加统计能力，降低成本和伦理风险，因为需要接受劣质治疗的患者较少。这些方法还能更好地适应试验进展过程中不可避免地出现的意外变化。在目标1中，我们将开发新的方法来利用现有的历史数据。在目标2中，我们查看了更一般的问题，即当子种群不可交换时，跨相关子种群的组合推理。在这里，我们的方法是基于子群体的随机聚类。最后，目标3涉及开发可公开分发的用户友好的计算机代码。最终结果将是临床试验更快得出结论，更少的患者被随机分配到较差的治疗。我们的方法和软件应该立即对实践产生影响，不仅是对早期研究，也是对大型验证性试验。从长远来看，审判合规性和应计利润也应有所改善。

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.