POWRE: Methods for the Analysis of Data with Multiple Levels of Correlation and a Comparison of Several Fundamental Statistical Approaches

POWRE：多级相关数据分析方法以及几种基本统计方法的比较

• 批准号: 0074569
• 负责人: Justine Shults
• 金额: $ 7.5万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-15 至 2001-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0074569&HistoricalAwards=false
• 关键词: POWRE; Methods; Analysis; Data; Multiple

We will develop improved statistical approaches for the analysis of potentially non-Gaussian data with multiple levels of correlation, as might be encountered when repeated observations collected on siblings within families are correlated due to within subject, or within family, similarities. Our first objective is to fulfill the need in the literature for a relatively straightforward approach for analysis of multi-levelcorrelated data by extending the method of quasi-least squares (QLS) [Chaganty (1997), Shults and Chaganty (1998), and Chaganty and Shults (1999)]. We will implement QLS using a correlation model discussed in Shults (2000) that is a generalization of a structure proposed by Lefkopolou, Moore, and Ryan (1989). Motivational examples for our research include an international trial to promote exclusive breast-feeding (Morrow, Guerrero, Shults, et. al., 1999) and an ongoing study of Interstitial Cystitis at the University ofPennsylvania (Mazurick, Landis, et. al., 2000).Our next objective is to explore issues related to the benefits and implementation of approaches that use patterned correlation matrices to model association among outcomes. We will examine the impact of failure to specify an appropriate model for the correlation structure of our data, by considering several study designs and examining the loss of efficiency when the correlation structure has been incorrectly specified. Of particular interest will be the effect of ignoring one or more levels of correlation for data with multiple levels of association. Simulations will also be conducted to explore the effect of misspecification on the mean square error of the estimates of the regression and correlation parameters for small samples. We will then explore the development of improved guidelines for selection of an appropriate correlation structure when several plausible models are available.Our final objective is to explore the theoretical underpinnings of QLS and contrast QLS with pseudo-likelihood (PL, Carroll and Ruppert, 1998) and several other fundamental approaches that have been described in the statistical literature. We will then describe a modified PL approach that is closely related to QLS and will explore the development of this approach for several models for the correlation structure of our data.This POWRE project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences (DMS).

我们将开发改进的统计方法，以分析具有多个相关性的潜在非高斯数据，这是由于受试者内部或家庭内部相似性在家庭内部的兄弟姐妹中反复观察时可能会遇到的。我们的第一个目的是通过扩展准地面正方形的方法（QLS）[Chaganty（1997），Shults and Chaganty（1998），以及Chaganty and Shults（1999）]。我们将使用Shults（2000）中讨论的相关模型实施QL，这是对Lefkopolou，Moore和Ryan（1989）提出的结构的概括。我们研究的动机例子包括一项国际试验，以促进独家母乳喂养（Morrow，Guerrero，Shults等，等，1999），以及Pennsylvania University ofpennsylvania大学（Mazurick，Landis，Landis等，等，2000，2000年）进行的间质性膀胱炎的持续研究。我们的下一个目标是探索与途中相关的方法，以促进与福利相关的方法。我们将通过考虑多个研究设计并检查相关结构的效率丧失，以指定数据的相关结构的适当模型，以指定不正确指定相关结构时，我们将研究未能指定适当的模型的影响。 特别令人感兴趣的将是忽略具有多个关联级别的数据的一个或多个级别的相关性。 还将进行仿真，以探索错误指定对小样本回归估计和相关参数的均方误差的影响。然后，我们将探讨在几个合理的模型时探索改进的指南，以选择适当的相关结构。我们的最终目标是探索QLS的理论基础，以及与伪样的QLS（PL，Carroll和Ruppert，1998）以及其他几种基本方法的QLS（PL，Carroll and Ruppert，1998）以及在统计学上所描述的。然后，我们将描述一种与QLS密切相关的修改后的PL方法，并将探讨该方法的多种模型的开发，以实现我们数据的相关结构。该POWRE项目由MPS多学科活动（OMA）和数学科学划分（DMS）共同支持。