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，随机临床试验的结果几乎总是使用某种形式的统计假设检验来分析。大多数用于分析临床试验的假设检验都假设了用于统计推断的总体模型，而实际上随机化模型更符合随机临床试验的实际进行方式。在分析临床试验时未考虑随机化模型可能会导致有效药物被宣布无效，无效药物被宣布有效。为了根据随机化模型分析临床试验，需要用于进行排列测试的复杂软件。这项研究的总体目标是开发灵活而健壮的软件，供统计学家或其他医学数据分析人员使用，用于对单临床或多临床随机临床试验进行排列测试。计算技术的不断进步为进行排列测试的软件开发创造了有利的环境。该项目包括与巴尔的摩县马里兰大学的罗森伯格博士合作，罗森伯格博士是基于随机化的推理和适应性设计方面的公认专家。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 a randomized clinical trial are almost always analyzed using some form of statistical hypothesis test. Most hypothesis tests used for analyzing clinical trials assume a population model for statistical inference, when in fact a randomization model is more consistent with the way randomized clinical trials are actually conducted. Failure to consider the randomization model when analyzing clinical trials can lead to effective drugs being declared ineffective, and ineffective drugs being declared effective. In order to analyze clinical trials in accordance with the randomization model, sophisticated software for conducting permutation tests is needed. The overall goal of this research is to develop flexible and robust software, usable by statisticians or other medical data analysts, for conducting permutation tests for single- or multi-clinic randomized clinical trials. The ongoing advances in computing technology have created a favorable climate for development of software for conducting permutation tests. This project includes a collaboration with Dr. Rosenberger of the University of Maryland, Baltimore County who is a recognized expert on randomization based inference and adaptive designs. PROPOSED COMMERCIAL APPLICATIONS: 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.