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

描述（由申请人提供）：临床试验变得越来越昂贵，患者越来越不愿意参加并坚持该方案，进一步增加了成本。我们建议开发贝叶斯统计方法和软件，以实现更高效、更符合伦理和更经济的临床试验。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，并且通常导致统计功效的增加以及成本和伦理风险的降低，因为需要暴露于较差治疗的患者较少。这些方法还能够更好地适应随着试验进展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，我们研究了当子群体不可交换时，在相关子群体之间组合推理的更一般问题。在这里，我们的方法是基于随机聚类的亚群。最后，目标3涉及开发可公开分发的方便用户的计算机代码。最终的结果将是临床试验更快地得出结论，更少的患者随机接受较差的治疗。我们的方法和软件不仅对早期研究，而且对大型验证性试验都有直接的影响。从长远来看，还应改善试验依从性和应计费用。 公共卫生相关性：这项工作与公共卫生的相关性在于它能够在临床试验中将较少的患者随机分配到对照组，因为这些组的可靠历史信息将被利用。因此，患者得到了更合乎道德的治疗，并更快地得出试验结论。这项工作对癌症、酒精和许多其他领域的干预措施的小型研究有潜在的巨大影响，这些领域的样本量很小，但存在其他信息来源，可以在保持科学完整性的同时加速这一过程。

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.