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，主要应答变量是至发生某些特定关注事件（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。本研究的目标是开发和扩展半参数贝叶斯模型和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，以处理存在复杂删失和不规则数据收集监测方案、存在错过临床访视和受试者存在不同类型复发事件风险以及不同原因失败的生存数据。半参数模型是限制性太强的参数模型和信息量太少的非参数模型之间的一种流行折衷。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）以及一个涉及一些参数的参数部分，如解释变量的回归系数。关于非参数部分的可用先验信息将被概括为一个随机过程，称为先验过程。参数零件上的可用先验信息将被建模为先验分布。本项目期间开发的方法将有助于分析复发事件数据、失败竞争原因的生存数据、受试者多个事件状态研究的生存数据、通过依赖于结果的不定期门诊访视测量的纵向数据和多个生活质量事件的生存数据。开发模型、相关的数据分析工具、相关的计算机程序和统计软件包代码、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中心作用。新的和现有的方法将进行评估和比较，主要是癌症研究数据的分析，从出版的文献和其他来源，如MUSC胡林斯癌症中心。

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.