STATISTICAL INNOVATIONS IN RISK MODELING

该项目的首要目标是通过以下方式进一步了解乳腺癌、结肠癌和卵巢癌： 开发和应用新的数据分析方法。具体而言，利用来自队列的数据， 在2006年进行的一项新的饮食验证研究中，我们将讨论与测量有关的目标 通过重复饮食评估进行误差校正。第二个目标涉及到审查的组合 端点。我们将改进方法，将多分类逻辑回归应用于项目1至 3、我们会将方法应用于特定主题，包括： a.乳腺导管癌与小叶乳腺癌的危险因素 B.饮食暴露（酒精、叶酸等）与乳腺癌的ER/PR状态 C.卵巢癌的危险因素 （i）.浆液性vs粘液性vs类浆液性/透明细胞卵巢癌 （ii）。粘液性与非粘液性卵巢癌 总死亡率的风险因素，包括心血管、癌症和其他终点，以及主要 癌症终点也将被单独检查。统计模型，假设相对稳定性 然而，当评估一个组合结果时， 经常使用这种模型。使用先前应用于乳腺和乳腺癌的对数发病率建模方法， 卵巢癌，我们将利用项目2的数据开发结肠癌模型。我们还将 评估乳腺癌风险因素背景下的因果推断，以及单倍型估计和基因环境 交互. 本项目中的研究人员与项目1、2和3中的研究人员密切合作， 导致了方法的发展和应用以及对癌症病因学的重要见解。继续 与研究特定癌症的研究人员合作将在今后的研究中发挥协同作用。 来自不同项目的调查人员之间的密切合作最大限度地提高了效率，提供了重要的 来自各种来源的投入，防止重复工作，并导致更连贯地介绍 终点之间的结果。

An overarching goal of this Project is to further understanding of breast, colon, and ovarian cancers, by developing and applying new methods for data analysis. Specifically, drawing on data from the cohort and from a new diet validation study to be conducted in 2006, we will address aims related to measurement error correction with repeated diet assessment. A second aim relates to the examination of combined endpoints. We will refine methods to apply polychotomous logistic regression to outcomes in Projects 1 to 3, we will apply methods to specific topics including: a. Risk factors for ductal vs. lobular breast cancer b. Dietary exposures (alcohol, folate, etc.) vs. ER/PR status in breast cancer c. Risk factors for ovarian cancers (i). Serous vs Mucinous vs. endometrioid / clear cell ovarian cancers (ii). Mucinous vs. non-mucinous ovarian cancers Risk factors for total mortality, including cardiovascular, cancer, and other endpoints, as well as leading cancer endpoints individually, will also be examined. Statistical models, which assume constancy of relative risks for a given risk factor, may be inappropriate when a combined outcome is being evaluated, though such models are often used. Using the methods of log-incidence modeling previously applied to breast and ovarian cancer, we will draw on data from Project 2 to develop a model for colon cancer. We will also evaluate causal inference in the context of breast cancer risk factors, and haplotype estimation and geneenvironment interactions. The close collaborations among investigators in this project with those in projects 1, 2, and 3, has led to the development and application of methods and important insights into cancer etiology. Continued collaborations with investigators addressing specific cancers will provide synergy in future investigations. The close work among the investigators from various projects maximizes efficiency, provides important input from a variety of sources, prevents duplication of effort, and results in a more coherent presentation of results across endpoints.