TREE-STRUCTURED SURVIVAL ANALYSIS--METHODS AND SOFTWARE

树结构生存分析--方法和软件

• 批准号: 3204531
• 负责人: MARK R SEGAL
• 金额: $ 6.43万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-15 至 1996-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3204531
• 关键词: breast neoplasms; computer program /software; female; human data; human mortality; method development; neoplasm /cancer epidemiology; statistics /biometry

Survival analysis is fundamental to cancer epidemiology. For example, breast cancer studies often use endpoints such as time to relapse or time to death. Yet very few data analytic tools exist for exploring such data. Thus, statistical assessments make recourse to routine use of Cox proportional hazards models, despite the strong assumptions embodied in this approach. Additionally, Cox models are not adept at identifying prognostic subgroups of patients. The purpose of this project is to further develop, and disseminate, methods and software for performing tree structured survival analysis. Tree techniques have appeal in that they are conceptually simple, readily interpretable and, being nonparametric, do not impose strong assumptions. Further, they are designed to identify distinct prognostic subgroups. These sub-groups are characterized by having common values of predictor variables thus making for clinically meaningful divisions. Subject to this constraint of like predictor values, the subgroups extracted possess maximally disparate survival prospects. Tree structured survival analysis has already been applied in a range of settings including evaluation of breast cancer markers. However, wider usage has been hampered by software deficiencies. Presently, only working code that is not user-friendly is available. The intention is to use this kernel as a basis for producing a comprehensive tree structured survival analysis program. This will involve writing refined input, output and help facilities. Algorithm efficiency will also be improved. Methodologic developments including means for handling missing data, time-varying predictors and assessing statistical significance will also be incorporated. Applications to two separate breast cancer survival studies will be pursued. The first involves projects falling under the umbrella of the SPORE in breast cancer awarded to UCSF which includes several marker studies. The second concerns an 18 year study of the prognostic value of breast fluid cytology being conducted by the UCSF Department of Epidemiology and Biostatistics.

生存分析是癌症流行病学的基础。例如, 乳腺癌研究通常使用终点，如复发时间或复发时间 直到死亡。然而，用于探索这类数据的数据分析工具却非常少。 因此，统计评估求助于常规使用的COX 比例风险模型，尽管 这种方法。此外，考克斯模型并不擅长识别 患者的预后亚组。 这个项目的目的是进一步开发和传播， 用于执行树状结构生存分析的方法和软件。 树技术具有吸引力，因为它们在概念上简单、容易 它是可解释的，而且是非参数的，不会强加很强的假设。 此外，它们旨在识别不同的预后亚组。 这些子群的特征是具有共同的预测值 因此，变量构成了具有临床意义的划分。受此影响 在相似预测值的约束下，所提取的子群具有 截然不同的生存前景。 树结构生存分析已经被应用于一系列 环境包括对乳腺癌标记物的评估。然而，范围更广 由于软件缺陷，使用受到了阻碍。目前，只有工作 用户不友好的代码也是可用的。我们的目的是利用这一点 作为产生全面树结构生存的基础的核 分析程序。这将涉及编写精炼的输入、输出和帮助 设施。算法效率也将得到提高。方法论 发展，包括处理丢失数据的方法，随时间变化 预测因素和评估统计意义也将是 注册成立。 两个独立的乳腺癌生存研究的申请将是 被追捕。第一个涉及的项目属于 包括几个标记物的乳腺癌孢子被授予加州大学旧金山分校 学习。第二项研究涉及一项长达18年的研究，研究的是高血压的预后价值。 加州大学旧金山分校乳房积液细胞学研究 流行病学和生物统计学。