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

基本信息

  • 批准号:
    0074569
  • 负责人:
  • 金额:
    $ 7.5万
  • 依托单位:
  • 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

项目摘要

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,用于分析具有多个相关性水平的潜在非高斯数据,因为当在家庭内的兄弟姐妹上收集的重复观察结果由于受试者内或家庭内的相似性而相关时,可能会遇到这种情况。我们的第一个目标是通过扩展准最小二乘法(QLS)[Chaganty(1997),Shults and Chaganty(1998),and Chaganty and Shults(1999)]来满足文献中对多水平相关数据分析的相对简单方法的需求。我们将使用Shults(2000)中讨论的相关模型来实现QLS,该模型是Lefkopolou,摩尔和Ryan(1989)提出的结构的推广。我们研究的动机例子包括一项促进纯母乳喂养的国际试验(Morrow,格雷罗,Shults等)。例如,1999)和宾夕法尼亚大学正在进行的间质性膀胱炎研究(Mazurick,Landis,et.例如,2000).我们的下一个目标是探索与使用模式化相关矩阵来模拟结果之间的关联的方法的益处和实施相关的问题。我们将通过考虑几种研究设计并检查当相关性结构被错误指定时的效率损失,来检查未能为我们的数据的相关性结构指定适当模型的影响。 特别感兴趣的是忽略具有多个关联级别的数据的一个或多个关联级别的影响。 还将进行模拟,以探索小样本的回归和相关参数估计值的均方误差的误设定的影响。我们的最终目标是探索QLS的理论基础,并将QLS与伪似然(PL,卡罗尔和Ruppert,1998年)和其他几种统计文献中描述的基本方法进行对比。然后,我们将描述一个修改后的PL方法,是密切相关的QLS,并将探讨这种方法的发展为几个模型的相关结构,我们的data.This POWRE项目是由MPS办公室的多学科活动(OMA)和数学科学(DMS)的部门联合支持。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Justine Shults其他文献

Dietary Patterns and Growth From 12 to 24 Months of Age in African American Infants
  • DOI:
    10.1093/cdn/nzab038_066
  • 发表时间:
    2021-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Meghan Shirley;Kyle Bittinger;Yun Li;Eileen Ford;Elliot Friedman;Jeffrey Gerber;Michal Elovitz;Andrea Kelly;Patricia DeRusso;Lindsey Albenberg;Danielle Drigo;Justine Shults;Rachel Walega;Hongzhe Li;Gary Wu;Babette Zemel
  • 通讯作者:
    Babette Zemel
strongDevelopment of a rigorous approach for retrospective natural history studies in leukodystrophies/strong
严格的方法进行回顾性自然历史研究的强度开发
  • DOI:
    10.1016/j.ymgme.2023.108041
  • 发表时间:
    2024-02-01
  • 期刊:
  • 影响因子:
    3.500
  • 作者:
    Anjana Sevagamoorthy;Francesco Gavazzi;Omar Sherbini;Ariel Vincent;Russel D'Aiello;Nicholson Modesti;Sylvia Mutua;Emily Yu;Sarah Woidill;Johanna L. Schmidt;Amy Pizzino;Justine Shults;Adeline Vanderver;Laura A. Adang
  • 通讯作者:
    Laura A. Adang
<strong>Validation of GMFC-MLD scale as a measure of gross motor function in metachromatic leukodystrophy</strong>
  • DOI:
    10.1016/j.ymgme.2023.107976
  • 发表时间:
    2024-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Sylvia Mutua;Anjana Sevagamoorthy;Francesco Gavazzi;Nivedita Thakur;Sarah Woidill;Emily Yu;Francesca Fumagalli;Samuel Groeschel;Genevieve Bernard;Chloe Stutterd;Christiane Kehrer;Lisa Emrick;Justine Shults;Adeline Vanderver;Laura A. Adang
  • 通讯作者:
    Laura A. Adang
<strong>Development of a rigorous approach for retrospective natural history studies in leukodystrophies</strong>
  • DOI:
    10.1016/j.ymgme.2023.108041
  • 发表时间:
    2024-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Anjana Sevagamoorthy;Francesco Gavazzi;Omar Sherbini;Ariel Vincent;Russel D'Aiello;Nicholson Modesti;Sylvia Mutua;Emily Yu;Sarah Woidill;Johanna L. Schmidt;Amy Pizzino;Justine Shults;Adeline Vanderver;Laura A. Adang
  • 通讯作者:
    Laura A. Adang
Ketamine Use in the Intubation of Critically Ill Children with Neurological Indications: A Multicenter Retrospective Analysis
  • DOI:
    10.1007/s12028-023-01734-0
  • 发表时间:
    2023-05-09
  • 期刊:
  • 影响因子:
    3.600
  • 作者:
    Mervin V. Loi;Jan Hau Lee;Jimmy W. Huh;Palen Mallory;Natalie Napolitano;Justine Shults;Conrad Krawiec;Asha Shenoi;Lee Polikoff;Awni Al-Subu;Ronald Sanders;Megan Toal;Aline Branca;Lily Glater-Welt;Laurence Ducharme-Crevier;Ryan Breuer;Simon Parsons;Ilana Harwayne-Gidansky;Serena Kelly;Makoto Motomura;Kelsey Gladen;Matthew Pinto;John Giuliano;Gokul Bysani;John Berkenbosch;Katherine Biagas;Kyle Rehder;Mioko Kasagi;Anthony Lee;Philipp Jung;Rakshay Shetty;Vinay Nadkarni;Akira Nishisaki
  • 通讯作者:
    Akira Nishisaki

Justine Shults的其他文献

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