Modeling and estimation of nonlinear quantile effects in child health research

 描述(由申请人提供)：在观察性和临床研究中，将给定结果的分布建模为一个或多个预测因素(或治疗或暴露)的函数通常令人感兴趣。例如，儿科生长曲线由一系列百分位数曲线组成，以模拟儿童某些身体尺寸作为年龄函数的分布。随着时间的推移，人类生长、某些疾病机制以及有害环境物质(如铅和汞)的影响等现象可能会显示出强烈的非线性。调查这种影响的数据收集通常涉及对同一对象进行一段时间的重复测量。例如，在临床试验中，现在通常会收集 生物标本在整个研究过程中不断重复。队列研究从个人出生或童年早期开始，在整个生命的延长阶段对其进行跟踪。混合效应模型代表了一种流行的统计方法，用于对纵向和其他类型的聚集数据进行建模。然而，均值回归中的正态假设意味着，一个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经验发现表明，在一些研究中并非如此。平均效应可能会抵消更强和更弱的效应，甚至抵消相反符号的效应。分位数回归(QR)是一种应用广泛的灵活的统计工具，近年来在医学领域迅速兴起。它有能力 允许对偏离“总体平均值”的受试者的暴露的影响进行推断，其对异常值和分布假设的稳健性，是QR在许多科学领域中成为成功工具的原因之一。虽然QR理论的基础已经奠定，但在纵向/聚集数据的非线性建模中的一些推断和计算问题仍有待探索。该项目旨在开发先进的统计方法，以解决儿科研究中在评估感兴趣的结果与一组预测因素之间的关联时经常遇到的复杂问题。我们建议发展非线性分位数混合模型(NLQMM)来模拟数据是纵向或聚集的情况下的非线性分位数效应。所提出的方法将具体处理三个方面：偏离正态分布、非线性和聚类。这一项目以国际和平研究所开发的既定方法和软件为基础，将填补统计文献中的一个空白，并将提供创新的分析方法，以解决包括医学和卫生科学在内的若干领域的重要研究问题。

 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.