Fu Haoda, Zhou Jin, Faries Douglas E
Eli Lilly and Company, Lilly Corporate Center, Indianapolis, 46285, IN, U.S.A.
Biostatistics Department, University of Arizona, Tucson, AZ, 85721, U.S.A.
Stat Med. 2016 Aug 30;35(19):3285-302. doi: 10.1002/sim.6920. Epub 2016 Feb 18.
With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study. Copyright © 2016 John Wiley & Sons, Ltd.
随着新的治疗方法和新技术的出现，个性化医疗已成为医疗产品开发新时代的重要组成部分。用于个性化医疗和亚组识别的传统统计方法主要集中在单一治疗或双臂随机对照试验上。受结果加权学习框架近期发展的启发，我们提出了一种替代算法来搜索与亚组识别问题相关的治疗分配。我们的方法专注于临床试验中的应用，以生成易于解释的结果。该框架能够处理来自随机对照试验和观察性研究的两种或两种以上治疗。我们用C++实现了我们的算法，并将其与R连接。通过模拟评估其性能，我们将我们的方法应用于一项糖尿病研究的数据集。版权所有© 2016约翰·威利父子有限公司。
