Statistical Methods in Chronic Disease Research

描述(由申请人提供)：这项研究的广泛、长期目标是为慢性疾病研究的设计和分析开发创新和高影响的统计方法，重点是基因组学。这一竞争性更新应用的具体目标包括：(1)对一般两阶段研究的有效估计，其中在第一阶段中对所有研究对象测量可能不完整的多变量结果和廉价协变量，而在第二阶段中使用第一阶段信息来最佳地选择对象以测量昂贵的协变量；(2)当所有研究对象在SNP阵列上进行基因分型但只对一小部分子集进行测序时或当未观察到的等位基因特定拷贝数直接相关时，对遗传关联进行有效和高效的分析；(3)随机效应模型下的Meta分析，当研究数量相对于研究样本量较小时，基于不同模型结构下多项研究的汇总统计进行变量选择。所有这些问题都是由首席研究员的应用研究经验推动的，与当前的基因组研究高度相关。提出的解决方案是基于似然性和其他合理的统计原理。新方法的大样本性质将通过现代经验过程理论和半参数有效性理论严格建立。将开发高效和稳定的数值算法来实现推理过程。建议的方法将通过模拟真实数据的模拟研究进行广泛评估，并应用于几项主要的基因组研究，其中大多数研究是在北卡罗来纳大学进行的。将免费提供带有适当文档的高效、可靠和用户友好的软件。这项研究不仅将推动生物统计学和统计遗传学领域的发展，还将影响北卡罗来纳大学和其他地方的慢性病研究。

DESCRIPTION (provided by applicant): The broad, long-term objectives of this research are the developments of innovative and high-impact statistical methods for the designs and analysis of chronic disease studies, with an emphasis on genomics. The specific aims of this competing renewal application include: (1) efficient estimation for general two-phase studies, in which possibly incomplete multivariate outcomes and inexpensive covariates are measured on all study subjects in the first phase and the first-phase information is used to optimally select subjects for measurements of expensive covariates in the second phase; (2) valid and efficient analysis of genetic association when all study subjects are genotyped on a SNP array but only a small subset is sequenced or when unobserved allele-specific copy numbers are of direct interest; (3) meta-analysis under random-effects models when the number of studies is small relative to study sample sizes and variable selection based on summary statistics of multiple studies under a variety of model structures. All these problems are motivated by the principal investigator's applied research experiences and are highly relevant to current genomic studies. The proposed solutions are based on likelihood and other sound statistical principles. The large-sample properties of the new methods will be established rigorously via modern empirical process theory and semiparametric efficiency theory. Efficient and stable numerical algorithms will be developed to implement the inference procedures. The proposed methods will be evaluated extensively through simulation studies mimicking real data and be applied to several major genomic studies, most of which are carried out at the UNC. Efficient, reliable and user-friendly software with proper documentation will be freely available. This research will not only advance the fields of biostatistics and statistical genetics but also influence chronic disease research at the UNC and elsewhere.