Suppr超能文献

临床开发中的效率机会:自适应临床试验设计和创新机器学习工具概述,心血管领域的实例。

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field.

机构信息

Early Development Biostatistics, Novartis Pharmaceuticals Corporation, USA.

Department of Pharmaceutical Biosciences, Uppsala University, Sweden.

出版信息

Contemp Clin Trials. 2021 Jun;105:106397. doi: 10.1016/j.cct.2021.106397. Epub 2021 Apr 16.

Abstract

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.

摘要

现代数据分析工具和统计建模技术在临床研究中越来越多地被用于提高诊断效果、评估疾病进展和预测治疗结果。但研究设计的重要性似乎被低估了,因为它会对研究成本、时间和统计效率产生严重影响。本文概述了临床试验中不同类型的适应性设计及其在心血管试验中的应用。我们强调了过去二十年中适应性设计工作的快速发展,包括一些关于复杂试验设计和主方案的最新监管指南。我们还描述了机器学习的作用日益增强,以及如何使用元启发式算法来构建越来越复杂的适应性设计,或者识别有趣的特征,以提高预测和分类能力。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验