Statistical Methods for Genetic Epidemiology

描述（由申请人提供）：越来越需要具有成本效益的癌症预防和控制战略，强调风险最高的人群亚群，以抵消国家和全球卫生成本。拟议的研究将通过在三个具体目标中帮助识别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测量方法，可以集中在特定的人口子群体上。特别是，我们将开发和评估更好的模型精度（校准）措施，以及可能和不太可能产生不利结果的模型区分。第二个目标是开发新的方法来扩大流行病学数据在评估模型性能方面的效用。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差；使用和解释病例对照数据来评估模型歧视；评估同一个体多重竞争不良结果的风险。第三个目标是增加免费的基于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测量，并允许调查人员将它们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.