临床试验对新药研发过程至关重要。由于临床试验需要大量时间和金钱的投入，试验设计者在设计试验前仔细调查试验环境至关重要。我们利用ClinicalTrials.gov的试验文档来了解成功和不成功的癌症药物试验的共同特点，以提供关于要学习和避免什么的见解。在这项研究中，我们首先将癌症药物试验在计算机中分类为成功和不成功的案例，然后利用自然语言处理从试验文档中提取符合条件的标准信息。为了提供可解释且可能可修改的新试验设计建议，我们使用对比挖掘法来发现在成功（完成并进入下一阶段）和不成功（暂停、撤回或终止）组之间的流行度差异显著的高度对比模式。我们的方法识别出了由九种主要癌症的药物类别、符合条件的标准、研究组织和研究设计的组合构成的对比模式。除了对挖掘出的对比模式进行定性验证的文献综述外，我们还发现，基于前200个对比模式作为特征表示的对比模式分类器，在我们的实验中可以实现约80%的F1分数，覆盖了十种癌症中的八种。总之，与ClinicalTrials.gov的现代化努力保持一致，我们的研究表明，了解成功和不成功的癌症试验的对比特征可能为试验研究员的决策过程提供见解，从而促进改善癌症药物试验的设计。版权所有©2023作者。 Elsevier Inc.保留所有权利。

Clinical trials are essential to the process of new drug development. As clinical trials involve significant investments of time and money, it is crucial for trial designers to carefully investigate trial settings prior to designing a trial. Utilizing trial documents from ClinicalTrials.gov, we aim to understand the common characteristics of successful and unsuccessful cancer drug trials to provide insights about what to learn and what to avoid. In this research, we first computationally classified cancer drug trials into successful and unsuccessful cases and then utilized natural language processing to extract eligibility criteria information from the trial documents. To provide explainable and potentially modifiable recommendations for new trial design, contrast mining was applied to discoverhighly contrasted patterns with a significant difference in prevalence between successful (completion with advancement to the next phase) and unsuccessful (suspended, withdrawn, or terminated) groups. Our method identified contrast patterns consisting of combinations of drug categories, eligibility criteria, study organization, and study design for nine major cancers. In addition to a literature review for the qualitative validation of mined contrast patterns, we found that contrast-pattern-based classifiers using the top 200 contrast patterns as feature representations can achieve approximately 80% F1 score for eight out of ten cancer types in our experiments. In summary, aligning with the modernization efforts of ClinicalTrials.gov, our study demonstrates that understanding the contrast characteristics of successful and unsuccessful cancer trials may provide insights into the decision-making process for trial investigators and therefore facilitate improved cancer drug trial design.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.