NEW STATISTICAL METHODS FOR CANCER SURVEILLANCE

提供了 癌症监测在癌症预防和干预中起着至关重要的作用。该提案发展 新的统计方法，处理癌症监测研究中复杂的数据相关问题。在 特别是，具体目标的动机是在监测研究中遇到的问题， 癌症死亡率和地理模式，并研究不成比例的疾病负担，特别是 人口和重要风险因素。我们计划 (1)发展新的方法，以分析变化趋势的相互关系矩阵[例如： 1969-2004年期间多个癌症部位的死亡率或发病率的变化（ARC）]; (2)为受审查的结果提出疾病聚类/监测方法; (3)提出了一种新的测试统计空间聚类检测，结合延迟分布， 与癌症有关，并研究疾病聚集模式是否因遗传因素而异。 特点; (4)发展和评估一个时空隐马尔可夫模型的疾病监测的基础上，区域特定的 疾病发生率; (5)开发高效的算法和用户友好的统计软件，用 目的是将其传播给健康科学研究人员。 所提出的方法将应用于几个癌症和环境健康项目， 研究人员已经参与了SEER癌症死亡率数据，SEER前列腺癌 发病率数据和台湾白血病数据。这些方法将允许从业者以及医疗保健 政策制定者更好地了解癌症死亡/发病率的变化趋势和相互关系 为了规划和资源分配的目的，这些趋势。这些方法也将有助于揭示 风险人群的不成比例的疾病负担，并确定重要的风险因素，包括遗传因素， 易感性本项目中提出的监测方法与时空方法相关联 项目1中提出的正则化回归模型，本项目中提出的正则化回归模型与 项目3中提出的变量选择方法。此外，这三个项目都有一个共同的主题， 分析高维观测研究数据，所有项目将产生统计方法， 计算方法，将告知那些在其他发展。

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.