Community-Based Studies in Cancer and Environment

癌症与环境的社区研究

• 批准号: 8037794
• 负责人: Yi Li
• 金额: $ 6.75万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-14 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8037794
• 关键词: Accounting; Allergens; Cancer Care Outcomes Research and Surveillance Consortium; Cancer Prevention Trial; Communities; Cox Models; Data; Data Analyses; Databases; Dependence; Environment; Environmental Epidemiology; Environmental Health; Event; Failure; Funding; Home environment; Incidence; Intervention Studies; Kenya; Malaria; Malignant Neoplasms; Measurement; Methodology; Modeling; Observational Study; Play; Population; Principal Investigator; Procedures; Property; Randomized; Recurrence; Research; Role; Sampling; Smoking Cessation Intervention; Structure; Survival Analysis; Time; United States National Institutes of Health; base; cancer epidemiology; flexibility; frailty; hazard; stem; theories

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 PI 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 non negligible 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 playa central role in all the specific aims. Available data stem from several cancer and environmental health projects that the PI 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.

这是一个更新的应用方法研究在统计问题相关的社区或观察性研究癌症和环境流行病学。针对多变量生存数据，提出了一类一般的半参数转换模型，提供了一个统一的似然框架，使回归系数具有总体水平的解释，并方便地对依赖参数进行建模。具体而言，PI计划开发：(1)多变量生存数据的半参数正态变换Cox模型：将开发成对依赖的灵活建模和一个