STATISTICAL ANALYSIS OF MULTIPLE EVENT TIME DATA

多个事件时间数据的统计分析

• 批准号: 2007848
• 负责人: David Oakes
• 金额: $ 8.55万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-07-01 至 2000-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2007848
• 关键词: mathematical model; model design /development; neoplasm /cancer epidemiology; statistics /biometry

Many cancer studies (animal, clinical and epidemiological) lead to event- time data in which multiple events, possibly of different types can occur to each subject or to each of a group of related subjects, for example animals born in the same litter. Examples of such data include time to tumor detection, time from remission to relapse into an acute disease phase, and times to discontinuation of experimental medications. Methods for the statistical analysis of such data will need to take into account heterogeneity between subjects or between groups of related subjects. This can be achieved by the incorporation of additional random effects into the standard survival models. The present research focusses on models involving "frailties", unobserved random proportionality factors applied to the hazard function. Methods of parametric, semiparametric and nonparametric estimation in such models will be investigated and applied to multiple event data arising from studies of cancer and other diseases. Bayesian methods, often involving the use of the Gibbs sampler will be considered as well as classical frequentist techniques. Emphasis will be placed on the formulation of models in such a way that covariate effects can be interpreted both conditionally on the random effect, and unconditionally. Frailty models will be used to investigate selection biases in epidemiologic studies and in the analysis of compliance data in clinical trials. Methods will be proposed for analysis of data when there is more than one plausible time scale, for example in a mortality study of asbestos workers one may want to use both calendar time and cumulative exposure to asbestos dust. In a study of the development of tumors in experimental animals, both the time from the start of the study and the time since the last tumor may be relevant. A general class of multiplicative intensity models involving multiple time scales will be studied. Characterizations of multivariate' survival distributions will be explored with a view to deriving graphical methods of assessing goodness of fit. The performance of a new algorithm for estimating a bivariate survival distribution subject to a general pattern of censoring will be investigated.

许多癌症研究（动物，临床和流行病学）导致事件- 可能发生不同类型的多个事件的时间数据 每个受试者或一组相关受试者中的每一个，例如 出生在同一窝的动物。此类数据的示例包括： 肿瘤检测，从缓解到急性疾病复发的时间 阶段和至停止实验药物的时间。方法 对于这些数据的统计分析将需要考虑到 受试者之间或相关受试者组之间的异质性。这 可以通过将额外的随机效应结合到 标准生存模型目前的研究集中在模型 涉及“虚弱”，应用未观察到的随机比例因子 危险函数。参数、半参数和 非参数估计在这样的模型将进行研究和应用 从癌症和其他疾病的研究中产生的多个事件数据。 贝叶斯方法，往往涉及使用吉布斯抽样将 被认为是经典的频率论技术。重点将 以这样一种方式放置在模型的制定中， 可以根据随机效应有条件地解释， 无条件 脆弱性模型将用于研究选择偏差， 流行病学研究和临床依从性数据分析 审判当有更多的数据时， 比一个合理的时间尺度，例如在死亡率研究， 石棉工人可能希望使用日历时间和累积时间 接触石棉粉尘。在一项研究中， 实验动物，无论是从研究开始的时间和 距离最后一个肿瘤的时间可能是相关的。的一般类别 涉及多个时间尺度的乘法强度模型将是 研究了 将探讨多变量生存分布的特征 以便导出评估拟合优度的图形方法。 一种新的双变量生存估计算法的性能 受一般审查模式约束的分布将是 研究了