ADAPTATION OF NEW STATISTICAL IDEAS FOR MEDICINE

这项研究资助的长期目标是带来新的发展 解决实际问题的统计理论和方法 生物统计学和医学。 研究人员从他们的角度追求这一目标 他们在斯坦福大学医学院担任联合职务，从事特定的工作 生物统计项目以及开发和审查。 研究 赠款涉及创建新的统计方法、研究新的和 通过数学分析和计算机模拟现有技术， 并将这些程序尝试应用于正在进行的研究项目 医学院。 以下五个领域将是重点 研究：（1）引导方法，（2）递归分区和 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。 成功的新统计方法在广泛的领域得到应用 生物医学应用范围。 根据以往的经验， 这里研究的主题对于癌症治疗临床特别有用 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.