Semiparametric Bayesian Survival Analysis

描述(由申请人提供)：通常在癌症和其他医学研究中，主要的反应变量是发生某些感兴趣的特定事件(例如癌症复发、患者死亡等)的时间。这种数据被称为生存数据。这项研究的目标是开发和扩展半参数贝叶斯模型以及相关的全贝叶斯和经验贝叶斯方法，用于分析从各种生物医学研究中获得的生存数据。半参数模型和相关方法将足够复杂，能够在存在复杂的审查和不规则的数据收集监测方案、错过就诊以及面临不同类型复发事件和不同原因失败的风险的受试者的情况下处理生存数据。半参数模型是一种流行的介于过于受限的参数模型和过于缺乏信息的非参数模型之间的折衷模型。半参数模型有一个非参数部分(一个未知函数，如基线风险或强度函数)和一个参数部分，包括几个参数，如解释变量的回归系数。关于非参数零件的可用先验信息将被总结为一个随机过程，称为先验过程。关于参数零件的可用先验信息将被建模为先验分布。在这个项目中开发的方法论将用于分析复发事件数据、与失败原因相互竞争的生存数据、来自多个事件状态PAR受试者的研究的生存数据、通过结果相关的不定期临床访问测量的纵向数据以及来自多个生活质量事件的生存数据。模型的开发、相关的数据分析工具、统计资料包的相关计算机程序和代码、广泛的模拟研究以及用于验证关键建模假设的诊断工具将在每个具体目标中发挥核心作用。新的和现有的方法将主要使用已发表文献中的癌症研究数据和其他来源(如MUSC Hoolings癌症中心)的分析来评估和比较。

DESCRIPTION (provided by applicant): Often in cancer and other medical studies, the primary response variable is the time to the occurrence of some particular event of interest (e.g. relapse of cancer, death of the patient, etc.). This kind of data is called survival data. The goals of this proposed research are to develop and extend semiparametric Bayesian models and associated full Bayes and empirical Bayes methodologies for the analysis of survival data obtained from various biomedical studies. The semiparametric models and associated methods will be sophisticated enough to deal with survival data in the presence of complex censoring and irregular data- collection monitoring schemes, in the presence of missed clinic visits and with subjects at the risk of recurrent events of different types as well as failure from different causes. Semiparametric models are a popular compromise between the too restrictive parametric and too non-informative nonparametric models. A semiparametric model has a nonparametric part (an unknown function such as a baseline hazard or an intensity function) as well as a parametric part involving a few parameters, such as regression coefficients for explanatory variables. The available prior information on the nonparametric part will be summarized as a stochastic process, called a prior process. The available prior information on the parametric part will be modeled as a prior distribution. The methodology developed during this project will be useful for the analysis of recurrent events data, survival data with competing causes of failures, survival data from the studies with multiple event states par subject, longitudinal data measured via outcome-dependent irregular clinic visits and survival data from multiple quality of life events. Development of models, associated data analytic tools, related computer programs and codes for statistical packages, extensive simulation studies, and diagnostic tools for verifying the key modeling assumptions will play central roles in each specific aim. New and existing methods will be evaluated and compared using analysis of mainly cancer studies data from the published literature and from other sources such as the MUSC Hoolings Cancer Center.