Smoothing Methods to Investigate Non-linear Effect in Correlated Data Studies

研究相关数据研究中非线性效应的平滑方法

• 批准号: 7106987
• 负责人: Edward C Chao
• 金额: $ 9.96万
• 依托单位: DATA NUMERICA INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-15 至 2006-12-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7106987
• 关键词: computer program /software; computer system design /evaluation; data collection methodology /evaluation; human data; longitudinal human study; outcomes research; statistics /biometry

DESCRIPTION (provided by applicant): Correlated data are very common in health studies. Such data could come from longitudinal studies, community panel surveys, genetic family studies or spatial studies. Typically, linear mixed-effect models are used for modeling continuous response, and generalized linear mixed models are applied to non-Gaussian data. In addition to such likelihood approaches, quasi-likelihood methods based on generalized estimating equations GEE are often used when the distributional assumption is not realistic and not easy to specify. We propose to extend these methods to handle the situations when the covariate effect is non-linear or is not easy to be modeled parametrically. This is similar to generalized additive models, where a smooth curve is used to predict the impact of a covariate on a univariate outcome. The goal of this study is to develop statistical software for correlated data in two areas. The first is the spline smoothing methods for generalized additive mixed models, which combine the semiparametric methods in generalized additive models using smoothing methods and mixed-effect modeling for correlated data. The second is the semiparametric GEE methods, which extend the GEE methods for correlated data with kernel smoothing to model the non-linear impact on health outcome. The research includes statistical methods, algorithm development and application to real health problems. The study requires analytic development on innovative semiparametric statistical methods and algorithm development on computational intensive methods. Currently, there is no software for these areas. The aim is to overcome this deficiency and extend the benefits of using smoothing methods to model non-linear covariate effect. The result is a software package, SmoothEffect, for handling correlated data. A comprehensive case study guidebook using problems from longitudinal studies and others will come with the software. Technical reports and simulation studies will also be developed. This study is to develop flexible statistical smoothing methods and software for analyzing correlated data or clustered data such as longitudinal data, panel surveys or spatial data. The focus of interest is to analyze such clustered data where records from the same experimental unit are related and the impact from some predictor on health outcome shows a non-linear smoothing curvature, which is no easy to be parameterized.

描述（由申请人提供）：相关数据在健康研究中非常常见。这些数据可以来自纵向研究、社区小组调查、遗传家族研究或空间研究。通常，线性混合效应模型用于连续响应建模，广义线性混合模型应用于非高斯数据。除了这种似然方法，基于广义估计方程GEE的准似然方法经常用于分布假设不现实且不容易指定的情况。我们建议扩展这些方法来处理协变量效应是非线性的或不容易参数化建模的情况。这类似于广义加性模型，其中平滑曲线用于预测协变量对单变量结果的影响。本研究的目标是开发两个领域相关数据的统计软件。第一种是广义可加混合模型的样条光滑方法，它将广义可加模型中的半参数光滑方法与相关数据的混合效应建模方法结合起来，联合收割机。第二种是半参数GEE方法，它扩展了GEE方法的相关数据与核平滑模型的健康结果的非线性影响。该研究包括统计方法，算法开发和应用到真实的健康问题。这项研究需要创新的半参数统计方法和计算密集型方法的算法开发的分析发展。目前，还没有针对这些领域的软件。其目的是克服这一不足，并扩大使用平滑方法的好处，以模拟非线性协变量的影响。结果是一个软件包，平滑效果，用于处理相关数据。一个全面的案例研究指南使用的问题，从纵向研究和其他人将与软件。还将编写技术报告和模拟研究报告。本研究旨在开发灵活的统计平滑方法和软件，用于分析相关数据或聚类数据，如纵向数据，面板调查或空间数据。感兴趣的焦点是分析这样的聚类数据，其中来自同一实验单元的记录是相关的，并且来自某些预测因子对健康结果的影响显示出非线性平滑曲率，这是不容易参数化的。