ADAPTATION OF NEW STATISTICAL IDEAS FOR MEDICINE

这项研究拨款的长期目标是在 统计理论和方法论对中国经济发展中的实际问题 生物统计学和医学。研究人员从他们的研究中追求这一目标 斯坦福医学院的联合职位，在那里他们致力于 生物统计学项目，以及发展和审查。关于网络信息技术的研究 GRANT涉及创建新的统计方法，研究新的和 现有技术通过数学分析和计算机模拟， 并将这些程序试用于正在进行的研究项目 医学院。以下五个领域将是本次会议的重点 研究：(1)Bootstrap方法，(2)递归划分和 分类树，(3)现代回归方法及其扩展， (4)图像重建方法；(5)危险率和删失数据。 成功的新统计方法得到了广泛的应用 生物医学应用的范围。根据过去的经验， 本文所研究的课题将在癌症治疗临床中特别有用。 试验、心血管研究和步态分析等领域 诊断措施往往具有非常复杂的结构。

The long-term goal of this research grant is to bring new developments in statistical theory and methodology to bear on the practical problems of biostatistics and medicine. The researchers pursue this goal from their joint positions in the Stanford medical school, where they work on specific biostatistical projects, and the development and review. Research on the grant involves the creation of new statistical methods, study of new and existing techniques through mathematical analysis and computer simulation, and trial applications of these procedures to ongoing research projects at the medical school. The following five areas will be the focus of the research: (1) bootstrap methods, (2) recursive partitioning and classification trees, (3) modern regression methods and their extensions, (4) image reconstruction methods, (5) hazard rates and censored data. Successful new statistical methodology finds application across a wide spectrum of biomedical applications. On the basis of past experience, the topics studied here will be particularly useful in cancer therapy clinical trials, cardiovascular research, and areas like gait analysis where diagnostic measures tend to have very complicated structure.