Integration and Evaluation of Pooled Cancer Studies with Heterogeneity

具有异质性的汇总癌症研究的整合和评估

• 批准号: 8628809
• 负责人: Mengling Liu
• 金额: $ 21.05万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-01 至 2015-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8628809
• 关键词: Address; Cancer Etiology; Cancer Model; Case-Control Studies; Cohort Studies; Collaborations; Communities; Complex; Computer software; Data; Diet; Disease; Disease model; Duct (organ) structure; Epidemiologic Studies; Etiology; Evaluation; Event; Grant; Health; Heterogeneity; Hormones; Investigation; Joints; Knowledge; Lead; Likelihood Functions; Malignant Neoplasms; Malignant neoplasm of ovary; Meta-Analysis; Methodology; Methods; Mission; Modeling; Nested Case-Control Study; New York; Ovarian; Population; Procedures; Public Domains; Randomized Clinical Trials; Research; Research Personnel; Research Project Grants; Resources; Risk Factors; Sample Size; Solutions; Staging; Statistical Methods; Structure; Sweden; Techniques; Testing; Time; Translating; Universities; Women' s Health; analytical tool; anticancer research; cancer risk; cancer type; case control; cohort; computerized tools; cost; data format; design; innovation; insight; loss of function; model development; novel; open source; prospective; public health relevance; sound; tool

DESCRIPTION (provided by applicant): A major challenge in cancer epidemiologic studies, especially those of rare cancers, is observing enough cases. To address this issue, researchers often pool multiple studies (or cohorts) to achieve large sample sizes, allowing them for increased power to study complex hypotheses. Combining studies, however, renders it difficult to analyze the pooled data in the presence of heterogeneity. A simple pooled analysis, which increases statistical power for detecting risk factors with homogenous effects, can misrepresent and obscure heterogeneous effects. Statistical solutions to this problem are limited and addressed mainly by two-stage methods that combine study-specific estimates using fixed- or random-effects models. Moreover, when a large number of risk factors are under investigation, in addition to identifying important ones, it is important to distinguish predictors with homogeneous versus heterogeneous effects. Knowing this structure can provide insight into disease etiology and have important implications for developing and evaluating cancer risk models using the pooled study strategy. However, statistical tests for detecting heterogeneity generally are of low power and not amenable to handle multivariate or high- dimensional risk factors. In this project, motivated by a collaborative nested case-control (NCC) study of ovarian cancer between the New York University Women Health Study (NYUWHS), the Northern Sweden Health and Disease Study (NSHDS), and the Italian Hormones and Diet in the Etiology of Cancer Study (ORDET), we will investigate the novel use of penalty regularization ideas to handle heterogeneity in the context of pooled NCC studies. We propose the following Specific Aims: (1) to develop an adaptive L1/Lq penalty regularized partial likelihood approach to integrating information from multiple NCC studies to identify important predictors, (2) to develop an adaptive L1 + L1/Lq penalty regularized partial likelihood approach to discovering the homogeneous and heterogeneous structure of predictors in pooled NCC studies, and (3) to translate the proposed procedures into practical knowledge and accessible software. As more and more research is conducted through collaborations from multiple studies, cohorts and centers, novel statistical methodology for integrating information across multiple studies is imperative. The proposed project will yield new statistical methodologies, which are theoretically sound and empirically effective, to con- duct pooled analysis, develop cancer risk models using the pooled study strategy, and evaluate existing models readily across multiple populations. Furthermore, the newly developed statistical methodology will be integrated into open-source software, providing practitioners with effective tools to analyze pooled studies. The developed methods will be applicable to many pooled studies, and lead to identify new risk factors related to cancers and a better understanding of the heterogeneity of effects for some cancer risk factors.

描述（由申请人提供）：癌症流行病学研究，尤其是罕见癌症的主要挑战是观察到足够的病例。为了解决这个问题，研究人员经常汇集多个研究（或队列）以实现大型样本量，从而使它们增加了研究复杂假设的能力。然而，将研究结合起来，很难在存在异质性的情况下分析汇集的数据。一项简单的合并分析，增加了统计能力，用于检测具有同质效应的危险因素，可能会歪曲和模糊的异质效应。该问题的统计解决方案受到限制，主要通过使用固定或随机效应模型结合研究特定估计的两阶段方法来解决。此外，当大量危险因素正在研究中，除了确定重要的危险因素外，重要的是要区分均质和异质效应的预测因子。了解这种结构可以洞悉疾病病因，并使用汇总研究策略对开发和评估癌症风险模型具有重要意义。但是，检测异质性的统计测试通常具有低功率，并且不适合处理多元或高维风险因素。 在这个项目中，纽约大学妇女健康研究（NYUWHS），瑞典北部健康与疾病研究（NSHDS）以及意大利荷尔蒙和饮食癌症研究（ORDET）中的一项卵巢癌的协作病例对照（NCC）研究（NYUWHS）之间的卵巢癌。 We propose the following Specific Aims: (1) to develop an adaptive L1/Lq penalty regularized partial likelihood approach to integrating information from multiple NCC studies to identify important predictors, (2) to develop an adaptive L1 + L1/Lq penalty regularized partial likelihood approach to discovering the homogeneous and heterogeneous structure of predictors in pooled NCC studies, and (3) to translate the proposed procedures into practical knowledge和可访问的软件。 随着越来越多的研究是通过多项研究，人群和中心的合作进行的，必须进行多个研究跨多个研究信息的新型统计方法。拟议的项目将产生新的统计方法，这些方法在理论上是合理的且在经验上有效的，以进行汇总分析，使用汇总研究策略开发癌症风险模型，并在多个人群中轻松评估现有模型。此外，新开发的统计方法将集成到开源软件中，为从业人员提供有效的工具来分析合并研究。开发的方法将适用于许多汇总研究，并导致确定与癌症有关的新危险因素，并更好地了解某些癌症危险因素的影响的异质性。