Study of exposures and biomarkers in cancer epidemiology

 描述(由申请人提供)：众所周知，不同的个体对相同的治疗有不同的反应，遗传基因因素在这些个体间的差异中发挥了作用。这种被称为预测遗传因素的遗传因素开始使医生能够根据患者的遗传程序量身定做治疗和干预措施，从而做出明智的治疗决定。当遗传因素与治疗或干预之间存在相互作用时，这意味着治疗的益处根据遗传因素的水平而有所不同。因此，流行病学研究越来越多地试图在统计模型中调查基因治疗、基因暴露和基因-基因相互作用，以确定有前景的预测遗传因素。尽管在Identifi阳离子方面取得了显著的进展 在癌症的病因风险因素中，识别相互作用和预测遗传因素的成功率仍然很低。虽然样本量的限制可能在一定程度上导致了这一挑战，但一些显著的fi不能相互作用无法复制，因为它们在生物学上可能是不可信的。因此，提高检测交互作用的能力并开发方法来识别实际可解释的交互作用和预测遗传因素是 在fi领域的关键需求中。虽然有大量的工作在评估二元结果的交互作用，但其他更丰富的数据类型也在出现，这些环境迫切需要评估预测遗传因素的分析方法。我们建议的首要目标是发展正式的统计和数学基础，以满足这些需求。在这个R01项目中，我们建议表明，在与致癌的定量表达相对应的统计模型中产生的相互作用可以以一种简明的方式编写，可以提供对疾病结果相对于风险因素的增加速度的洞察。我们建议开发创新和强大的频率和贝叶斯统计技术，通过利用模型简约性的显著fi潜势来评估相互作用。我们建议使用这些强大的方法来开发校准良好的模型，以确定临床上可解释的预测遗传因素。我们还建议开发和传播实现我们建议的方法的R库。我们专注于开发计数结果(在单个时间点和两个时间点测量)和在单个时间点测量多个连续结果的方法。我们将把我们建议的方法应用于三项合作研究的数据--儿童痣研究，以及脑和乳腺癌患者的认知研究--并使用验证数据集验证fi结果。

 DESCRIPTION (provided by applicant): It is well recognized that different individuals respond in different ways to the same treatment, and inherited genetic factors play a role on these inter-individual differences. Such genetic factors, referred to as predictive genetic factors, are beginning to enable physicians to make informed therapeutic decisions by tailoring treatments and interventions according to the genetic profiles of patients. When there is an interaction between a genetic factor and treatment or intervention, it means that treatment benefits vary according to the level of the genetic factor. Therefore, epidemiology studies increasingly try to investigate gene-treatment, gene-exposure, and gene-gene interactions in statistical models to identify promising predictive genetic factors. Despite remark- able progress in the identification of etiologic risk factors for cancer, the success rate of identifying interactions and predictive genetic factors remains low. While sample size limitations may partly contribute to this challenge, some significant interactions cannot be replicated because they may be biologically implausible. Therefore, improving the power to detect interactions and developing methodologies to identify practically interpretable interactions and predictive genetic factors are among the critical needs of the field. While there is a large and growing body of work on evaluating interactions for binary outcomes, other richer data types are also be- coming available, and analytic methods to evaluate predictive genetic factors are urgently needed for these settings. The overarching objective of our proposal is to develop formal statistical and mathematical foundations to address these needs. In this R01 project, we propose to show that interactions arising in statistical models corresponding to quantitative expressions for carcinogenesis can be written in a parsimonious manner that can provide insights into the rate at which disease outcome increases in relation to the risk factors. We propose to develop innovative and powerful frequentist and Bayesian statistical techniques to evaluate interactions by harnessing the significant potential of model parsimony. We propose to use these powerful methods to develop well-calibrated models to identify clinically interpretable predictive genetic factors. We also propose to develop and disseminate R libraries that implement our proposed methods. We focus on developing methodologies for count outcomes (measured at a single time point and at two time points) and multiple continuous outcomes measured at a single time point. We will apply our proposed methods to data from three collaborative studies - the study of nevi in children, and cognitive studies of brain and breast cancer patients - and confirm our results using validation data sets.