Regression trees for some problems with multi-dimensional data

研究者为一些具有复杂和高维数据的重要问题开发回归树解决方案。复杂性包括缺失、删失、混合变量类型和在随机时间点进行的相关测量。一个具体的问题是识别亚组的差异治疗效果的比较试验，涉及随时间变化的协变量。第二个问题是重要性评分和阈值的变量和第三个是检测的差异测试项目功能的测试和评估。主要的方法依赖于适应和扩展的GUIDE决策树算法，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，以及确保用于分裂树节点的变量的无偏选择。以更快的速度收集和生成更大量数据的能力给数据分析和解释带来了新的困难。例如，健康行业正在研究使用遗传信息和随着时间的推移反复观察来寻找个性化的疾病治疗方法。拟议的研究将扩展基于决策树的统计方法，以解决以下问题：（i）基于随时间对健康和其他结果的重复观察，识别从一种治疗中受益多于另一种治疗的患者亚群，（ii）识别遗传和其他变量并就其在疾病预测中的重要性及其与治疗的相互作用进行排序，及（iii）在测试及评估中识别因性别、种族或社会经济及文化背景而歧视他人的测试项目。 决策树模型具有易于应用和直观解释的独特优势。后一个属性对于理解和推进科学至关重要。

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.