Community-Based Studies in Cancer and Environment

描述（由申请人提供）：这是一个更新申请，用于与癌症和环境流行病学中基于社区或观察性研究相关的统计问题的方法学研究。一般类的半参数变换模型提出了多变量生存数据，提供了一个统一的似然框架，允许回归系数有人口水平的解释和依赖参数方便地建模。具体而言，主要研究者计划开发：（1）多变量生存数据的半参数正态变换考克斯模型：将开发成对依赖的灵活建模，并提出一类新的边缘化脆弱模型。半参数极大似然估计方法将进行研究。我们将进一步考虑广义半参数正态变换生存模型，包括比例风险和比例优势模型作为特殊情况;（2）多变量生存数据的加速半参数正态变换模型：我们将通过探索失效时间的平均结构，提出一个新的半参数正态变换AFT模型。（3）具有不可忽略治愈分数的多变量生存模型：提出了两类新的模型：半参数脆弱治愈模型和半参数正态变换治愈模型。这两类模型的理论属性将被探讨和可行的推理程序将被调查。扩展到一个空间设置将被考虑;（4）covariate测量错误和延迟进入的重复发生事件模型：两个新的类的重复发生事件模型将被提出来解释集群内的相关性，误测量协变量和延迟进入。属性的模型将进行调查，并提出各种推理程序。 经验数据分析将在所有具体目标中发挥核心作用。现有数据来自主要研究者参与的几个癌症和环境健康项目，即NCI-funded Cancer Care Outcomes Research and Surveillance Consortium（CanCORS），Young Worker Smoking Cessation Intervention study（一项随机社区癌症预防试验），NIH SEER癌症发病率数据库，West Kenya Malaria Study和Home Allergen Study。虽然应用问题的动机，拟议的研究提供了有用的贡献，一般统计理论和方法，在生存分析，多元统计分析，治愈模型和测量误差建模。

DESCRIPTION (provided by applicant): This is a renewal application for methodological research in statistical issues related to community-based or observational studies in cancer and environmental epidemiology. A general class of semiparametric transformation models is proposed for multivariate survival data, providing a unified likelihood framework that allows the regression coefficients to have population-level interpretations and the dependence parameters to be conveniently modeled. Specifically, the principal investigator plans to develop: (1) Semiparametric normal transformation Cox models for multivariate survival data: Flexible modeling of pairwise dependence will be developed and a new class of marginalized frailty models will be proposed. Semiparametric maximum likelihood estimation approach will be investigated. We will further consider generalized semiparametric normal transformation survival models, encompassing the proportional hazards and the proportional odds models as special cases; (2) Accelerated semiparametric normal transformation models for multivariate survival data: We will propose a new semiparametric normal transformation AFT model by exploring the mean structure of the failure times. We will develop consistent and numerically stable inference procedures, and investigate their large sample results; (3) Multivariate survival models with nonnegligible cure fractions: Two new classes of models, semiparametric frailty cure models and semiparametric normal transformation cure models will be proposed. Theoretical properties for both classes of models will be explored and feasible inference procedures will be investigated. The extension to a spatial setting will be considered; (4) Clustered recurrent event models with covariate measurement errors and delayed entry: two new classes of recurrent event models will be proposed to account for within-cluster correlations, mismeasured covariates and delayed entry. Properties of the models will be investigated and various inference procedures will be proposed. Empirical data analyses will play a central role in all the specific aims. Available data stem from several cancer and environmental health projects that the principal investigator has been involved in, namely, the NCI-funded Cancer Care Outcomes Research and Surveillance Consortium (CanCORS), the Young Worker Smoking Cessation Intervention study (a randomized community-based cancer prevention trial), the NIH SEER cancer incidence database, the West Kenya Malaria Study and the Home Allergen Study. Although motivated by applied problems, the proposed research offers useful contributions to general statistical theory and methodology in survival analysis, multivariate statistical analysis, cure models and measurement error modeling.