Regression trees for some problems with multi-dimensional data

多维数据的一些问题的回归树

• 批准号: 1305725
• 负责人: Wei-Yin Loh
• 金额: $ 13万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305725&HistoricalAwards=false
• 关键词: Regression; trees; some; problems; multi

The investigator develops regression tree solutions for some important problems with complex and high-dimensional data. Complexity includes missingness, censoring, mixed variable types, and correlated measurements taken at random time points. One specific problem is identification of subgroups for differential treatment effects in comparative trials involving time-varying covariates. A second problem is importance scoring and thresholding of variables and a third is detection of differential test item functioning in testing and evaluation. The main approach relies on adapting and extending the GUIDE decision tree algorithm to these problems. Expected difficulties and challenges include minimizing error rates and computational cost as well as ensuring unbiased selection of the variables used to split the nodes of the trees.The ability to collect and generate greater amounts of data at faster speeds creates new difficulties to data analysis and interpretation. For example, the health industry is looking into using genetic information and repeated observations over time to find personalized treatments for diseases. The proposed research will extend a statistical approach based on decision trees to solve problems such as: (i) identifying subpopulations of patients who benefit more from a one treatment over another, based on repeated observations on health and other outcomes over time, (ii) identifying and ranking genetic and other variables with respect to their importance in prediction of illness and their interactions with treatments, and (iii) identifying test items in testing and evaluation that discriminates against people due to their gender, race, or socio-economic and cultural background. A decision tree model has the unique advantage that it is easy to apply and intuitive to interpret. The latter property is crucially important to understanding and advancing the science.

研究者为复杂高维数据的一些重要问题开发了回归树解决方案。复杂性包括缺失、审查、混合变量类型和在随机时间点进行的相关测量。一个具体的问题是在涉及时变协变量的比较试验中鉴别不同治疗效果的亚组。第二个问题是变量的重要性评分和阈值问题，第三个问题是测试和评估中差异测试项目功能的检测问题。主要的方法依赖于对GUIDE决策树算法进行调整和扩展来解决这些问题。预期的困难和挑战包括最小化错误率和计算成本，以及确保用于分割树节点的变量的无偏选择。以更快的速度收集和生成更多数据的能力给数据分析和解释带来了新的困难。例如，健康行业正在研究利用遗传信息和长期反复观察来找到个性化的疾病治疗方法。建议的研究将扩展基于决策树的统计方法来解决以下问题：（一）根据长期对健康和其他结果的反复观察，确定从一种治疗比另一种治疗获益更多的患者亚群；（二）根据预测疾病的重要性及其与治疗的相互作用，确定遗传变量和其他变量并对其进行排序；（三）确定检测和评估中因性别、种族或社会经济和文化背景而歧视人们的测试项目。决策树模型具有易于应用和直观解释的独特优势。后一种性质对于理解和推进科学至关重要。