Survival Analysis Methods for Transplantation Studies

移植研究的生存分析方法

• 批准号: 7255981
• 负责人: DOROTA M DABROWSKA
• 金额: $ 25.1万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7255981
• 关键词: Blood; Bone Marrow; Characteristics; Class; Data; Data Analyses; Disease Progression; Disease regression; Event; Failure; Follow-Up Studies; Kidney; Kidney Transplantation; Marrow; Matched-Pair Analysis; Methods; Modeling; Outcome; Patients; Process; Property; Recording of previous events; Regression Analysis; Research; Risk; Stochastic Processes; Survival Analysis; Time; Transplantation; United Network for Organ Sharing; base; experience; follow-up; international center; prognostic

DESCRIPTION (provided by applicant): The project develops models for analysis of multiple event times. Two projects are considered in this proposal. The first project proposes new methods for regression analysis of a class of stochastic process models called modulated renewal processes. They can be used to model multiple event times representing specific episodes in the disease progression of a patient. In this project we propose extensions of accelerated failure time and transformation models. We also consider parameter estimation in these models. The second project considers matched pair competing risk regression models. Models of this kind arise in biomedical and actuarial follow-up studies. In such studies, matching (or blocking) serves as a method to control for effects of prognostic variables on the variability of the outcomes represented by time and failure type. We propose a class of stochastic process models for analysis of matched pair competing risks. Both projects develop also applications to analysis of follow-up data from bone marrow and kidney transplant studies. We consider prediction of patient post-transplant experience based on pre-transplant characteristics and follow-up history.

描述（由申请人提供）：该项目开发用于分析多个事件时间的模型。本提案考虑了两个项目。第一个项目提出了一类称为调制更新过程的随机过程模型的回归分析的新方法。它们可用于对代表患者疾病进展中特定事件的多个事件时间进行建模。在这个项目中，我们提出了加速故障时间和转换模型的扩展。我们还考虑了这些模型中的参数估计。第二个项目考虑配对竞争风险回归模型。这类模型出现在生物医学和精算后续研究中。在这些研究中，匹配（或区组）作为一种方法来控制预后变量对时间和失败类型所代表的结局变异性的影响。本文提出了一类随机过程模型用于分析配对竞争风险。这两个项目还开发了骨髓和肾移植研究后续数据分析的应用程序。我们考虑根据移植前的特征和随访史预测患者的移植后经历。