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。这项研究的目的是开发和扩展半参数贝叶斯模型和方法，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机制、不规则数据收集时间表和缺失数据的生存数据。特别是在生存分析中，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折衷。半参数模型有一个非参数部分（未知函数，如风险或强度函数）以及一个涉及少数参数的参数部分，如解释变量的回归系数或衡量总体异质性的参数。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组计数数据（当在临床访视期间记录复发事件计数时）、治愈概率为阳性的生存数据、预防试验的生存数据、受试者多结局研究的生存数据和疫苗试验的生存数据。开发模型、相关数据分析工具和模拟方法以及用于验证建模假设的诊断工具将在每个具体目标中发挥核心作用。将使用已发表文献和其他来源的数据对新方法和现有方法进行评价和比较。

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 aim of this proposed research is to develop and extend semi-parametric Bayesian models and methodologies for the analysis of survival data obtained from various biomedical studies. The Semi-parametric models and associated methods will be sophisticated enough to deal with survival data in presence of complex censoring mechanisms, irregular data-collection schedules and missing data. Particularly in survival analysis, semi-parametric models present a popular compromise between the too restrictive parametric and too non-informative nonparametric models. A semi-parametric model has a nonparametric part (an unknown function such as a hazard or an intensity function) as well as a parametric part involving a few parameters, such as regression coefficients for explanatory variables or parameters gauging the heterogeneity in the population. 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 the panel count data (when counts of recurrent events are recorded during clinic visits), survival data with a positive probability of cure, survival data from the prevention trials, survival data from the studies with multiple outcomes par subject and survival data from the vaccine trials. Development of models, associated data analytic tools and simulation methods, and diagnostic tools for verifying the modeling assumptions will play central roles in each specific aim. New and existing methods will be evaluated and compared using data from the published literature and from other sources.