NEW STATISTICAL METHODS FOR CANCER SURVEILLANCE

癌症监测的新统计方法

• 批准号: 8132890
• 负责人: Yi Li
• 金额: $ 15.56万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8132890
• 关键词: Address; Algorithms; Cancer Prevention Intervention; Cessation of life; Characteristics; Complex; Computer software; Data; Data Analyses; Detection; Disease; Disease Clusterings; Environmental Health; Failure; Genetic; Genetic Predisposition to Disease; Goals; Health Policy; Health Sciences; Incidence; Individual; Lasso; Linear Programming; Link; Malignant Neoplasms; Malignant neoplasm of prostate; Methodology; Methods; Modeling; Modern Medicine; Monitor; Multiple Cancer Sites; Observational Study; Outcome; Patients; Pattern; Play; Policy Maker; Population; Populations at Risk; Proxy; Recording of previous events; Research; Research Personnel; Resort; Resource Allocation; Risk Factors; Role; Sampling; Signal Transduction; Spatial Distribution; Statistical Methods; Surveillance Methods; Survival Analysis; Taiwan; Techniques; Testing; Time; base; burden of illness; cancer site; density; flexibility; leukemia; markov model; mortality; novel; residence; statistics; stem; surveillance data; surveillance study; theories; trend; user-friendly

PROVIDED. Cancer surveillance plays an essential role in cancer prevention and intervention. This proposal develops new statistical methods that deal with complex data-related issues in cancer surveillance studies. In particular, the specific aims are motivated by problems encountered in surveillance studies that monitor cancer mortality and geographical patterns, and that study disproportionate disease burden on particular populations and important risk factors. We plan to (1) develop new methods to analyze the cross-relationship matrix of the change trends [e.g. the annual rate changes (ARC)] in mortality or incidence on multiple cancer sites for the period of 1969-2004; (2) propose disease clustering/surveillance methods for outcomes subject to censoring; (3) propose a new test statistic for spatial clustering detection that incorporates latency distributions that are associated with cancer, and studies whether disease clustering patterns differ according to genetic characteristics; (4) develop and evaluate a spatio-temporal hidden Markov model for disease surveillance based on regionspecific counts of disease incidence; (5) develop efficient algorithms and user-friendly statistical software that implement these methods with the goal of disseminating them to health science researchers. The proposed methods will be applied to several cancer and environmental health projects that the investigators have been involved in, namely, the SEER cancer mortality data, the SEER prostate cancer incidence data and the Taiwan Leukemia data. The methods will allow practitioners as well as health care policy makers to better understand the change trends of cancer deaths/incidence and the cross-relationship of these trends for the purpose of planning and resource allocation. The methods will also help reveal disproportionate disease burden on at-risk populations and identify important risk factors, including genetic susceptibility. The surveillance methods proposed in this project are linked to the spatio-temporal methods proposed in Project 1, and the regularized regression models proposed in this project are related to the variable selection methods proposed in Project 3. In addition, all three projects have a common theme of the analysis of high-dimensional observational study data, and all projects will generate statistical methods and computational approaches that will inform those developed in the others.

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