Measurement Error, Nutrition and Breast/Colon Cancer

描述： 这项拟议的功绩延期是对当前奖励的合乎逻辑的延续，围绕 新统计方法的发展及其在癌症和营养研究中的应用。 将考虑以下广泛的主题。 ？对饮食摄入量数据的分析：与NCI的研究人员合作，我们开发了 获取大量令人兴奋的饮食摄入量数据集，包括一项主要的生物标记物研究，两项主要的 监测研究和一项主要的前瞻性队列研究。我们的“NCI-方法”估计 通常的食物摄取一次使用一种食物：我们将通过开发方法来提高效率 多种食物同时食用。此外，健康饮食指数(HEI)等问题也激发了人们的需求 同时对多种食物摄入量和营养素进行建模：我们将开发这些模型，还 用统计方法对它们进行拟合。 饮食与结肠癌发生：我们将开发半参数统计方法 化学功能数据来分析一系列在细胞水平上进行的研究，涉及饮食、细胞凋亡、 细胞反应与结肠癌的发生。我们的新方法，基于一种新的函数公式。 传统主成分，允许理解细胞在结肠隐窝中的位置的影响， 以及合并加密信号，即加密本身之间的响应相关性。 半参数方法：我们将开发一系列新的统计方法，其动机是 基因-环境相互作用研究的问题。首先，在病例对照研究中，我们已经表明 如果人们能够假设遗传和环境因素是 在人口中独立的，可能在考虑到人口战略的因素后- 形象化。我们将开发新的收缩方法，允许有效的基因环境推断 在独立的情况下，各阶层都站得住脚，但这对偏离这一假设是稳健的。 第二，我们将考虑主要关注是否存在遗传影响的研究， 但基因与环境的相互作用是有可能的。我们将开发新的分数类型测试 在这种情况下，对于遗传效应，谨慎地使用投影确保有效的推断。在……里面 在许多基因或SNP的情况下，我们将再次使用收缩思想来提高性能 记分型测试。这项工作将扩展到相加模型和重复测量/纵向 数据。

Description: This proposed MERIT extension is a logical continuation of the current award, revolving around the development of new statistical methods and their application to studies involving cancer and nutrition. The following broad topics will be considered. ¿ Analysis of Dietary Intake Data: In conjunction with researchers at the NCI, we have developed access to a number of exciting dietary intake data sets, including a major biomarker study, two major surveillance studies and a major prospective cohort study. Our "NCI-Method" for estimating the usual intake of foods uses one food at a time: we will get greater efficiency by developing methods for multiple foods simultaneously. Also, issues such as the healthy eating index (HEI) motivate the need to model multiple food intakes and nutrients simultaneously: we will develop those models and also statistical methods to fit them. ¿ Diet and Colon Carcinogenesis: We will develop semiparametric statistical methods for hierar- chical functional data to analyze a series of studies, done at the cellular level, involving diet, apoptosis, cellular response and colon carcinogenesis. Our new approach, based on a novel formulation of func- tional principal components, allows understanding of the effects of cell position in the colonic crypts, as well as incorporating crypt signahng, i.e., correlations of response among the crypts themselves. ¿ Semiparametric Methods: We will develop a series of novel statistical methods motivated by issues of gene-environment interaction studies. First, in case-control studies, we have shown that great gains in efficiency can be made if one can assume that genetic and environmental factors are independent in the population, possibly after conditioning on factors to account for population strat- ification. We will develop novel shrinkage approaches that allow efficient gene-environment inference with independence given strata holds but that are robust to deviations from this assumption. Second, we will consider studies for which the main interest is in whether there are genetic effects, but there is the possibility for gene-environment interaction. We will develop novel score-type tests for genetic effects in this context, where the careful use of projection ensures efficient inference. In the case of many genes, or SNPs, we will again use shrinkage ideas to improve the performance of score-type testing. This work will be extended to additive models and repeated measures/longitudinal data.