Community-Based Studies in Cancer and Environment

描述(由申请人提供)：这是一份关于癌症和环境流行病学的社区研究或观察性研究的统计问题的方法学研究的续期申请。针对多变量生存数据，提出了一类通用的半参数变换模型，提供了一个统一的似然框架，使得回归系数具有总体水平的解释，并且相关参数可以方便地建模。具体地说，主要研究人员计划发展：(1)多变量生存数据的半参数正态变换Cox模型：将发展灵活的两两相关模型，并将提出一类新的边际脆弱模型。研究半参数最大似然估计方法。我们将进一步考虑广义半参数正态变换生存模型，包括比例风险模型和比例赔率模型作为特例；(2)多元生存数据的加速半参数正态变换模型：通过探索失效次数的平均结构，我们将提出一个新的半参数正态变换AFT模型。我们将开发一致的和数值稳定的推理程序，并研究其大样本结果；(3)具有不可忽略的治愈分数的多元生存模型：将提出两类新的模型，半参数脆弱曲线模型和半参数正态变换曲线模型。将探索这两类模型的理论性质，并研究可行的推理程序。将考虑对空间环境的扩展；(4)具有协变量测量误差和延迟进入的集群重复事件模型：将提出两类新的重复事件模型来解释簇内相关性、错误测量协变量和延迟进入。将研究模型的性质，并提出各种推理过程。经验数据分析将在所有具体目标中发挥核心作用。现有数据来自首席研究员参与的几个癌症和环境健康项目，即NCI资助的癌症护理结果研究和监测联盟(CanCORS)、青年工作者戒烟干预研究(一项以社区为基础的随机癌症预防试验)、NIH SEER癌症发病率数据库、肯尼亚西部疟疾研究和家庭过敏研究。虽然这项研究的动机是应用问题，但它为生存分析、多元统计分析、治愈模型和测量误差建模的一般统计理论和方法提供了有益的贡献。

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.