Measurement Error, Nutrition and Breast/Colon Cancer

产品说明： 拟议的MERIT延期是当前奖励的合理延续，围绕 发展新的统计方法及其在癌症和营养研究中的应用。 将审议以下广泛的专题。 饮食摄入量数据分析：与NCI的研究人员合作，我们开发了 访问一些令人兴奋的饮食摄入量数据集，包括一个主要的生物标志物研究，两个主要的 监测研究和一项主要的前瞻性队列研究。我们的“NCI-Method”用于估计 通常的食物摄入量一次只吃一种食物：我们将通过开发方法来提高效率， 多种食物同时此外，健康饮食指数（HEI）等问题也激发了这种需求。 同时模拟多种食物摄入和营养：我们将开发这些模型， 统计方法来拟合它们。 饮食和结肠癌发生：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，包括饮食，细胞凋亡， 细胞反应和结肠癌发生。我们的新方法，基于一个新的公式的功能- 函数主成分，允许理解细胞在结肠隐窝中的位置的影响， 除了结合crypt签名，即，反应的相关性。 半参数方法：我们将开发一系列新颖的统计方法， 基因与环境相互作用研究。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，可能在考虑人口战略因素后， 化。我们将开发新的收缩方法，允许有效的基因-环境推理 具有独立性，给定层保持，但对偏离该假设是稳健的。 其次，我们将考虑主要关注是否存在遗传效应的研究， 但也存在基因与环境相互作用的可能性。我们将开发新的分数类型测试 在这种情况下，仔细使用投影确保有效的推理。在 在许多基因或SNP的情况下，我们将再次使用收缩的想法来提高性能， 分数类型测试。这项工作将扩展到加法模型和重复测量/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.