Permutation Test Software for Randomized Clinical Trials

随机临床试验(RCT)可以说是药物开发过程的关键，RCT的结果几乎总是使用某种形式的统计假设检验来分析。最常用的假设检验假设为统计推断的总体模型，即使随机化模型更符合随机对照试验的真实特征。总体模型检验返回的近似p值在某些情况下可能具有误导性，导致有效药物被宣布无效，或无效药物被宣布有效。为了支持使用适当的随机化模型进行随机对照试验的分析，需要复杂的软件来进行基于随机化的排列测试。计算技术的不断进步为这类软件的广泛使用创造了有利的环境。这项研究的目标是开发灵活和健壮的软件，用于对单临床或多临床随机对照试验进行基于随机化的排列测试。这一功能的一个子集已经在第一阶段预原型(“RTAnalyzer”)中成功实现。第二阶段旨在建立一个能够处理各种试验设计的全尺寸原型，包括使用自适应随机化的设计。第二阶段的项目包括与两位置换测试领域的专家合作：威廉·罗森伯格博士和邦妮·拉弗勒博士。拟议的商业应用：可以使用常规置换测试分析临床试验数据的软件将对学术界、政府以及制药、生物技术和医疗器械行业的临床研究组织具有明显的商业价值。关键应用是分析具有不寻常随机方案的临床试验、具有不寻常治疗反应模式的试验，以及标准分布假设无效的试验。

The randomized clinical trial (RCT) is arguably the linchpin of the drug development process, and the results of an RCT are almost always analyzed using some form of statistical hypothesis test. The most frequently used hypothesis tests assume a population model for statistical inference, even though a randomization model is more consistent with real-world characteristics of RCTs. Approximate p- values returned by population-model tests can, under certain circumstances, be misleading, resulting in effective drugs being declared ineffective, or ineffective drugs being declared effective. To support analysis of RCTs using the appropriate randomization model, sophisticated software for conducting randomization-based permutation tests is needed. Ongoing advances in computing technology have created a favorable climate for widespread use of such software. The goal of this research is to develop flexible and robust software for carrying out randomization-based permutation tests for single- or multi- clinic RCTs. A subset of this functionality has been successfully implemented in a Phase I pre-prototype ("RTAnalyzer"). Phase II seeks to build a full-scale prototype capable of handling a wide variety of trial designs, including designs using adaptive randomization. The Phase II project includes collaborations with two experts in the field of permutation testing: Dr. William Rosenberger and Dr. Bonnie LaFleur. PROPOSED COMMERCIAL APPLICATION: Software that can use general permutation tests to analyze clinical trial data would have clear commercial value to clinical research organizations in academia, Government, and the pharmaceutical, biotechnology, and medical device industries. Key applications are the analysis of clinical trials with unusual randomization schemes, trials with unusual patterns of treatment response, and trials where standard distributional assumptions are invalid.