SEMIPARAMETRIC BAYESIAN METHODS FOR SURVIVAL DATA

描述(改编自申请者摘要)：拟议的研究 使用与事件时间数据相关的生物统计方法(数据 生存时间、致癌生长检测次数、复发 症状次数等)。目标是开发更多的 这些数据的统计模型，以及相关的方法 统计分析。重点将放在半参数贝叶斯上 模型和方法。半参数性质允许相当大的 通用性和适用性，但有足够的结构来提供有用的物理 对医学中特定应用的解释和理解 研究。贝叶斯理论和计算的最新进展使 研究复杂模型和数据结构是可行的。四大类 将考虑事件时间数据的百分比：单变量生存数据 (一组无关患者的生存时间)，多事件时间 数据(不相关的一组中的每一组中的连续重复事件 患者)、多变量生存数据(一组患者的生存时间 有血缘关系的患者或 环境)，为每个患者提供两个独立的活动 (如感染和发病)。 每一个被考虑的模型都经过了某种形式的审查 机制，如正确的审查、分组(由于测量不准确 或定期跟踪)、间隔审查(由于错过跟踪)。 蒙特卡罗算法，包括数据增强和吉布斯抽样， 将用于处理模型的复杂性以及 对数据中存在的数据进行审查或分组。 研究方法包括数学建模、数学建模、数学建模 统计方法的发展，计算机算法的编写，以及 通过对已发表的癌症和癌症数据集的重新分析来举例说明 致癌物的其他医学研究和动物实验。模型 在这项研究中开发的方法应该会导致改进 对临床研究中事件时间数据的理解 癌症和其他疾病--包括事件时间与 各种危险因素和群体(家族性)的量化 依赖关系。

DESCRIPTION (Adapted from Applicant's Abstract): The proposed research is in biostatistical methods associated with event time data (data on survival times, detection times of carcinogenic growths, recurrence times of symptoms, etc.). The objective is to develop additional statistical models for such data, along with associated methods of statistical analysis. The focus will be on semiparametric Bayesian models and methods. The semiparametric nature allows considerable generality and applicability but enough structure for useful physical interpretation and understanding for particular applications in medical research. Recent advances in Bayesian theory and computations make the study of complex models and data structures feasible. Four categories of event time data will be considered: univariate survival data (survival times for a group of unrelated patients), multiple event time data (successive repeated events in each of a group of unrelated patients), multivariate survival data (survival times for a group of patients who are related to each other either genetically or environmentally), two independent events in tandem to every patient (such as infection and onset of disease). Each model considered has been subjected to some kind of censoring mechanism, such as right censoring, grouping (due to inexact measurement or periodic followup), interval censoring (due to missed followups). Monte Carlo algorithms, including data augmentation and Gibbs sampling, will be used to deal with the complexity of the model as well as the censoring or grouping present in the data. The research method includes mathematical modeling, mathematical developments of statistical methods, writing of computer algorithms, and exemplification by reanalysis of published data sets from cancer and other medical studies and animal experiments with carcinogens. Models and methods developed in this research should lead to an improved understanding of event time data occurring in clinical research in cancer and other diseases--including the relation of event times to various risk factors, and quantification of group (familial) dependencies.