Statistical Methods for Genetic Epidemiology

描述（由申请人提供）：越来越需要强调高风险人群亚组的具有成本效益的癌症预防和控制策略，以应对国家和全球卫生成本。拟议的研究将通过帮助确定三个具体目标的高风险个体来解决这一需求。第一个目标是开发更多信息的模型性能的措施，可以集中在特定的人口亚组。特别是，我们将开发和评估模型准确性（校准）的更好措施，以及那些可能和不可能发展的不利结果之间的模型区分。第二个目标是开发新的方法来扩大流行病学数据评估模型性能的效用。这些方法将允许研究者在评估模型性能时适应队列选择偏倚;使用和解释病例对照数据以评估模型区分;并评估同一个体中多种竞争性不良结局的风险。第三个目标是增加免费提供的基于R的软件RMAP（风险模型评估程序）http://www.stanford.edu/~ggong/rmap/index.html，以包括新的和更多信息的性能指标，并允许研究人员将其应用于广泛的流行病学数据。

DESCRIPTION (provided by applicant): Cost-effective cancer prevention and control strategies that emphasize population subgroups at highest risk are increasingly needed to counter national and global health costs. The proposed research will address this need by helping identify high-risk individuals in three specific aims. The first aim is to develop more informative measures of model performance that can focus on specific population subgroups. In particular, we will develop and evaluate better measures of model accuracy (calibration), and model discrimination between those likely and unlikely to develop the adverse outcome. The second aim is to develop new ways to expand the utility of epidemiologic data for assessing model performance. These methods will allow investigators to accommodate cohort selection bias in assessing model performance; use and interpret case-control data for assessing model discrimination; and assess risks of multiple competing adverse outcomes in the same individual. The third aim is to augment the freely available R-based software RMAP (Risk Model Assessment Program) http://www.stanford.edu/~ggong/rmap/index.html to include the new and more informative performance measures and allow investigators to apply them to a broad range of epidemiologic data.