Fence Methods for Mixed Model Selection: Theory and Applications

混合模型选择的栅栏方法：理论与应用

• 批准号: 8251124
• 负责人: Jiming Jiang
• 金额: $ 27.16万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8251124
• 关键词: Accounting; Address; Algorithms; Area; Bioconductor; Bioinformatics; Biological; Characteristics; Clinical Trials; Collaborations; Colon; Colon Carcinoma; Complex; Computer software; Data; Data Set; Development; Diagnostic; Disease; Disease Progression; Elements; Exons; Family; Funding; Gene Expression; Gene Expression Microarray Analysis; Genes; Genetic Databases; Geographic Locations; Goals; Government; Grouping; Health; Health Care Surveys; Health Planning; Health Surveys; Home environment; Human Genetics; Image; Institution; Knowledge; Laboratories; Libraries; Linear Regressions; Literature; Measures; Medical; Methodology; Methods; Microarray Analysis; Mind; Mission; Modeling; Outcome; Partner in relationship; Pathway interactions; Performance; Play; Population; Principal Investigator; Procedures; Property; Published Comment; RNA Splicing; Research; Role; Sampling; Science; Scientist; Series; Services; Signal Transduction; Small-Area Study; Source; Staging; Statistical Models; Structure; Subgroup; Surveys; System; Therapeutic; Time; United States National Institutes of Health; Variant; Work; Writing; base; cancer microarray; case control; design; high throughput analysis; improved; insight; interest; member; novel; novel therapeutics; programs; repository; simulation; software development; success; theories; user-friendly

DESCRIPTION (provided by applicant): This project aims to develop a new class of model selection strategies, known as fence methods. The general idea involves a procedure to isolate a subgroup of what are known as correct models (of which the optimal model is a member). This is accomplished by constructing a statistical fence, or barrier, to carefully eliminate incorrect models. Once the fence is constructed, the optimal model will be selected among the correct models (those within the fence) according to, e.g., simplicity of the models. The last step of the procedure, i.e., the selection of the optimal model within the fence, can be made exible to take scientific or economical considerations into account. The PIs have developed this concept within the context of mixed model selection which includes among other things, linear mixed models and generalized linear mixed models with clustered or non-clustered data. This project aims to: 1) Develop new fence methodology for the problem of gene set analysis from gene expression (microarray) studies. These gene sets represent apriori groupings of genes whose activity is thought to be related (often via biological pathways). Thus it is of interest of know if these groups are perturbed with respect to changing conditions like worsening of disease (in our case, worsening of colon cancer). Knowledge of this would provide insight into which pathways seem to be implicated in poor outcome versus better outcomes, thereby providing potentially novel bio- logical targets for diagnostics or therapeutics. Fence methods for gene set analysis provide a potentially rich class of approaches for tackling such a task. Aim 1 will develop in detail the theory and optimality of such approaches and then provide comprehensive comparisons to existing methods. The newly developed methods will then be applied to a large repository of colon cancer microarray data which represents the various stages of the disease. Working closely with a biological collaborator, implicated pathways found by the fence will be validated and unravelled biologically. 2) Develop new fence methodology for the problem of analyzing large scale health survey data with the problem of small area estimation in mind. In this case, fence methods will be developed along two tracks - the rst involves allowing a richer class of non-parametric small area estimation mixed models to be used where the degree of smoothing for the xed eects part of the model can be assessed by appropriate fence approaches, and the second involves developing a fence approach that allows one to choose amongst competing small area models based upon prediction quality of small area random effects. In both situations, theory for the fence methods will be developed and the area of application will be a large health care survey collected at NIH. 3) Extend fence methods. Extensions will include new computational approaches known as grating, and also new ways of implementing the fence for association studies with applications to large case-control SNP association studies. Again, detailed theory will be developed and applications undertaken with appropriate collaborators. 4) Develop freeware software to implement the fence methods that will be developed in this project. This software will be written in the statistical package R which will allow users to integrate with other software continually being developed around the world. PUBLIC HEALTH RELEVANCE: Correlated data is widely collected in all of the medical sciences from imaging data to longitudinal clinical trial data to family-based genetic data - all in an effort to better understand the underlying determinants of disease. Mixed models have provided a rich framework to model such data and make best use of the various kinds of structure that naturally are present. However, selecting from a set of competing mixed models has proven to be much more elusive of a problem with little guidance provided from the literature. The PIs of this proposal building on their recent successes in the area, oer a new elegant way to tackle this problem for complex data problems, and will rigorously study their proposed methods statistically, as well as through a variety of interesting applications via collaborations with prominent laboratories at their home institutions and outside. These applications include gene set analysis from gene expression (microarray) studies, association analysis from high throughput SNP studies, and small area estimation from large health survey data.

描述（由申请人提供）：该项目旨在制定新的模型选择策略，即围栏方法。总体想法涉及一个程序，以隔离所谓的正确模型的子组（其中最佳模型是成员）。这是通过构造统计围栏或障碍来仔细消除错误模型来实现的。一旦构建围栏，将根据模型的简单性（例如，在正确的模型（栅栏内）（栅栏内的模型）中选择最佳模型。该程序的最后一步，即，可以将最佳模型选择在栅栏内的选择，以考虑到科学或经济的考虑。 PI在混合模型选择的背景下开发了此概念，该概念包括线性混合模型和带有聚类或非簇数据的广义线性混合模型。该项目的目的是：1）为基因表达（微阵列）研究的基因集分析问题开发新的围栏方法。这些基因集代表了其活性被认为是相关的基因的Apriori组（通常是通过生物途径）。因此，对于不断变化的疾病恶化（在我们的情况下，结肠癌恶化），这些群体是否会受到扰动。对此的了解将提供有关哪些途径似乎与差的结果与更好的结果有关的洞察力，从而为诊断或治疗学提供了潜在的新型生物靶标。基因集分析的栅栏方法提供了解决此类任务的潜在富裕方法。 AIM 1将详细发展此类方法的理论和最佳性，然后与现有方法进行全面的比较。然后，新开发的方法将应用于代表疾病各个阶段的结肠癌微阵列数据的大量存储库。与生物合作者紧密合作，围栏发现的涉及途径将在生物学上得到验证和脱离。 2）开发新的围栏方法，以解决大规模健康调查数据的问题，并考虑到小面积估计的问题。在这种情况下，将沿着两条曲目开发围栏方法 - 第一个涉及允许使用更丰富的非参数小面积估计混合模型混合模型，可以通过适当的围栏方法评估该模型的一部分的平滑程度，而第二个涉及允许在小区域中竞争预测质量的小区域中的围栏方法中的第二种方法。在这两种情况下，都将开发围栏方法的理论，并且应用领域将是NIH收集的大型医疗保健调查。 3）扩展栅栏方法。扩展将包括称为光栅的新计算方法，以及与大型病例对照SNP协会研究应用的围栏研究的新方法。同样，将开发详细的理论，并与适当的合作者进行申请。 4）开发免费的软件软件来实现该项目将要开发的围栏方法。该软件将写在统计软件包R中，该软件包将允许用户与世界各地不断开发的其他软件集成。 公共卫生相关性：从成像数据到纵向临床试验数据到基于家庭的遗传数据的所有医学科学中，相关数据都广泛收集 - 所有这些都是为了更好地了解疾病的潜在决定因素。混合模型提供了一个丰富的框架来建模此类数据，并充分利用自然存在的各种结构。但是，从一组竞争的混合模型中选择，事实证明，在文献中提供的指导很少，因此难以捉摸的问题。该提案的PI是建立在他们最近在该地区取得成功的基础的，这是一种解决此问题的新优雅方法，以解决复杂的数据问题，并通过与各种有趣的应用程序在其家庭机构和外部进行了多种有趣的应用程序，并通过各种有趣的应用程序来严格研究其提议的方法。这些应用包括来自基因表达（微阵列）研究的基因集分析，高吞吐量SNP研究的关联分析以及大型健康调查数据的小面积估计。