Semiparametric inference for case-control studies with complex sampling

复杂抽样病例对照研究的半参数推理

• 批准号: 8740470
• 负责人: Yan Li
• 金额: --
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-24 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8740470
• 关键词: Accounting; Apoptosis; Case-Control Studies; Chemical Exposure; Complex; Computer software; Data; Diet; Differentiation and Growth; Environment; Environmental Risk Factor; Equilibrium; Etiology; Frequencies; Future; Gene Frequency; Genes; Genetic Predisposition to Disease; Haplotypes; Kaposi Sarcoma; Lead; Life Style; Logistic Regressions; Methods; Outcomes Research; Performance; Population; Renal carcinoma; Resources; Risk; Role; Sampling; Single Nucleotide Polymorphism; Statistical Methods; Susceptibility Gene; Weight; base; case control; design; genetic association; genetic variant; human disease; innovation; population based; public health relevance; simulation

DESCRIPTION (provided by applicant): SUMMARY Population-based case-control studies with complex sampling, e.g. stratified multistage cluster sampling, are now increasingly used to study the role of genetic variants and gene-environment (G-E) interplay in the etiology of human diseases. Retrospective- based logistic regression estimators have been developed to exploit various covariate- distributional assumptions to gain efficiency in such studies when cases and controls are selected with simple random sampling. These methods, however, can lead to invalid inferences when cases or controls are selected with complex sampling. Although most single nucleotide polymorphism (SNP)-based association studies with complex sampling account for the complications induced by complex designs, many of haplotype-based genetic association studies with complex sampling tend to ignore them in the estimation of haplotype frequencies, regression coefficients or both. In this project, we will develop statistical methods for taking into account the design complications in haplotype-based association studies with complex sample designs. Specifically, attracted by the efficiency advantage of the retrospective method, we will explore the assumptions of Hardy- Weinberg equilibrium and G-E independence, and develop an efficient estimator suitable for the case-control study with a complex sample design. On the other hand, analysis with above assumptions can be misleading when these assumptions fail. Thus, the above assumptions will be relaxed by proposing an empirical Bayes-type shrinkage estimator as a trade-off between bias and efficiency. The proposed methods will be evaluated using simulations under various complex sample designs as well as two population-based case-control studies. Furthermore, a unified software package will be developed to widely disseminate the research outcomes.

描述（由申请人提供）：总结复杂抽样的基于人群的病例对照研究，例如分层多阶段整群抽样，现在越来越多地用于研究遗传变异和基因-环境（G-E）相互作用在人类疾病病因学中的作用。当病例和对照采用简单随机抽样时，已经开发了基于回顾性的logistic回归估计，以利用各种协变量分布假设来提高此类研究的效率。然而，这些方法，可能会导致无效的推断时，案件或控制选择复杂的采样。虽然大多数基于单核苷酸多态性（SNP）的复杂抽样关联研究考虑了复杂设计引起的复杂性，但许多基于单倍型的复杂抽样遗传关联研究在估计单倍型频率、回归系数或两者时往往忽略了复杂设计引起的复杂性。在这个项目中，我们将开发统计方法，考虑到设计复杂的单体型为基础的关联研究与复杂的样本设计。具体而言，受回顾性方法的效率优势的吸引，我们将探讨哈代-温伯格平衡和G-E独立性的假设，并开发一个有效的估计，适用于病例对照研究的复杂样本设计。另一方面，当这些假设失败时，上述假设的分析可能会产生误导。因此，上述假设将通过提出经验贝叶斯型收缩估计作为偏差和效率之间的权衡来放松。所提出的方法将使用各种复杂的样本设计下的模拟以及两个基于人群的病例对照研究进行评估。此外，将开发一个统一的软件包，以广泛传播研究成果。