Modeling and estimation of nonlinear quantile effects in child health research

儿童健康研究中非线性分位数效应的建模和估计

• 批准号: 9109261
• 负责人: Marco Geraci
• 金额: $ 7.33万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9109261
• 关键词: Accelerometer; Accounting; Address; Affect; Age; Antihypertensive Agents; Archives; Biological; Birth; Blood Pressure; Child; Child health care; Childhood; Clinical; Clinical Research; Clinical Trials; Cohort Studies; Communities; Complex; Computer software; Computers; Computing Methodologies; Data; Data Collection; Dependence; Development; Disease; Economically Deprived Population; Educational workshop; Elements; Foundations; Growth; Health; Health Sciences; Human; Individual; Insulin; Intervention; Knowledge; Label; Lead; Life; Literature; Longitudinal Studies; Low Birth Weight Infant; Measurement; Medical; Medical Economics; Mercury; Methodology; Methods; Modeling; Morbidity - disease rate; Non-linear Models; Normalcy; Obesity; Observational Study; Outcome; Participant; Patients; Pediatric Research; Physical activity; Polynomial Models; Population; Public Health; Research; Risk Factors; Sampling; Science; Series; Specimen; Statistical Methods; Succimer; Time; Training; Work; analytical method; base; blood lead; coping; data modeling; design; early childhood; economic cost; experience; flexibility; follow-up; innovation; interest; lead exposure; longitudinal analysis; mortality; open source; physical inactivity; population based; public health relevance; randomized placebo controlled trial; sedentary; social exclusion; socioeconomics; software development; theories; tool; trend

 DESCRIPTION (provided by applicant): In observational and clinical studies, it is often of interest to model the distribution of a given outcome as a function of one or more predictors (or treatments, or exposures). For example, pediatric growth charts consist of a series of percentile curves to model the distribution of certain body measurements in children as a function of age. Phenomena like human growth, certain disease mechanisms and the effects of harmful environmental substances such as lead and mercury, may show strong nonlinearities over time. Data collection to investigate this kind of effects typically involves repeated measurements on the same subject for a period of time. In clinical trials, for example, it is now common to collect biological specimens repeatedly throughout the duration of the study. Cohort studies follow-up individuals from their birth or early childhood throughout extended periods of life. Mixed-effects models represent a popular statistical approach to the modeling of longitudinal and other types of clustered data. However, the normality assumptions in mean regression imply that all individuals in a population are affected by the exposure in the same exact way. A growing body of empirical findings in the medical literature shows that this is not the case in a number of studies. Mean effects may average out stronger and weaker effects, or even cancel out effects of opposite sign. Quantile regression (QR) is a flexible statistical tool with a vast number of applications and has been rapidly emerging in the medical field in the recent years. Its ability to allow inference about the effects of an exposure in subjects that deviate from the `population mean', its robustness to outliers and to distributional assumptions, are among the reasons why QR is a successful tool in many fields of science. Although the foundations of QR theory have been laid out, some inferential and computational issues in nonlinear modelling of longitudinal/clustered data in are yet to be explored. This project aims at developing advanced statistical methods to address complex issues often encountered in pediatric research when assessing the association between an outcome of interest and a set of predictors. We propose to develop nonlinear quantile mixed models (NLQMMs) to model nonlinear quantile effects when the data are longitudinal or clustered. The proposed methods will specifically cope with three aspects: deviations from normality, nonlinearity and clustering. By building on established methods and software developed by the PI, this project will fill a hole in the statistical literatue and will provide innovative analytical methods to address important research questions in a number of fields, including the medical and health sciences.

 描述（由申请人提供）：在观察性和临床研究中，通常感兴趣的是将给定结果的分布建模为一个或多个预测因子（或治疗或暴露）的函数。例如，儿科生长图表由一系列百分位数曲线组成，以模拟儿童中某些身体测量值随年龄的分布。人类生长、某些疾病机制以及铅和汞等有害环境物质的影响等现象可能会随着时间的推移表现出强烈的非线性。调查这种影响的数据收集通常涉及在同一受试者上重复测量一段时间。例如，在临床试验中， 在整个研究过程中重复使用生物样本。队列研究从出生或幼儿期开始，在整个生命的延长期内对个体进行随访。混合效应模型代表了一种流行的统计方法来建模纵向和其他类型的聚类数据。然而，均值回归中的正态性假设意味着群体中的所有个体都以相同的方式受到暴露的影响。医学文献中越来越多的实证研究结果表明，许多研究并非如此。平均效应可能会使较强和较弱的效应平均化，甚至会抵消相反符号的效应。分位数回归（QR）是一种灵活的统计工具，具有广泛的应用，近年来在医学领域迅速兴起。的能力 允许对偏离“总体平均值”的受试者的照射影响进行推断，其对离群值和分布假设的稳健性，是QR在许多科学领域成为成功工具的原因之一。虽然QR理论的基础已经奠定了，一些推理和计算的纵向/集群数据的非线性建模问题还有待探讨。该项目旨在开发先进的统计方法，以解决儿科研究中经常遇到的复杂问题，评估感兴趣的结果和一组预测因子之间的关联。我们建议发展非线性分位数混合模型（NLQORM）来模拟非线性分位数效应时，数据是纵向或集群。所提出的方法将具体科普三个方面：偏离正态性，非线性和聚类。通过建立在既定的方法和软件开发的PI，该项目将填补一个空白，在统计literatue，并将提供创新的分析方法，以解决重要的研究问题，在一些领域，包括医学和健康科学。