Statistical Methods for Analyzing Repeated Measures Data

分析重复测量数据的统计方法

• 批准号: 8941533
• 负责人: Paul Albert
• 金额: $ 15.4万
• 依托单位: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8941533
• 关键词: Accounting; Address; Affect; Area; Automobile Driving; Bayesian Method; Biological Markers; Biology; Clinical; Data; Data Analyses; Development; Environmental Risk Factor; Epidemiology; Event; Fetal Growth; Fetus; Growth; Institutes; Literature; Longitudinal Studies; Measurement; Measures; Menstruation; Methodology; Methods; Modeling; National Institute of Child Health and Human Development; Normal Range; Pattern; Prevention; Prevention Research; Research; Research Personnel; Statistical Methods; Statistical Models; Time; Ultrasonography; Work; base; design; fetal; follow-up; novel strategies; research study; sound; statistics

The analysis of repeated measures data is important for most studies in the Division of Epidemiology, Statistics, and Prevention Research (DESPR) and in the NICHD as a whole. There are a number of areas in which additional methodological research is needed in order to appropriate analyze data from research studies. Missing data is a common problem when analyzing data from longitudinal studies. Investigators need to account for this missing data when analyzing data from their studies. The development of new methods for analyzing continuous or discrete repeated measures data is important area of methodological research. Although there is a wide literature for a single longitudinal measurement, there has been little work done on appropriately accounting for missing data when the number of longitudinal measurements is large. We will development new methods for addressing this problem under this project. Developing approach for jointly modeling time-to-event and repeated measures is currently an active area of biostatistical research. Most of this work focuses on modeling a single repeated biomarker and the time to an event. Jointly modeling multiple repeated biomarkers and time to event data is an important, yet difficult problem. In this project, we will investigate this problem with both frequentist and Bayesian approaches. Important studies that will illustrate this methodology include the Biocycle study in the Epidemiology Branch and the Natural Driving Study in the Prevention Branch. Estimating the patterns in key biomarkers over the menstrual period is important for understanding the biology of the menstrual period. Also, important is understanding how environmental factors affect the relationship between these biomarkers over the cycle. The Bio-cycle study was designed to address these scientific issues. Using the Bio-cycle data as a motivating example, we will develop new approaches for analyzing such data. Understanding ranges of normal growth is an important research area within DESPR and in the institute as a whole. For example, studying ranges of normal fetal growth as measured by ultra-sound is important in identifying fetuses who are growing abnormally. For many of these ultra-sound longitudinal fetal studies, the number of measurements may be related to the underlying growth curve of the particular fetus since continued follow-up may be based on a clinical decision (i.e., more ultra-sound measurements may be taken on fetuses who grow slowly). This type of observation mechanism may result in informative number of measurements which needs to be taken into account in the statistical modeling. We will develop new methods for non-linear growth models and apply them to data from our fetal growth studies

重复测量数据的分析对于流行病学、统计和预防研究部门 (DESPR) 以及整个 NICHD 的大多数研究都很重要。 有许多领域需要进行额外的方法学研究，以便适当分析研究数据。 分析纵向研究数据时，缺失数据是一个常见问题。研究人员在分析研究数据时需要考虑这些缺失的数据。 开发分析连续或离散重复测量数据的新方法是方法论研究的重要领域。尽管有大量关于单个纵向测量的文献，但当纵向测量数量很大时，在适当考虑缺失数据方面所做的工作却很少。 我们将在这个项目下开发解决这个问题的新方法。 开发对事件发生时间和重复测量进行联合建模的方法是目前生物统计研究的一个活跃领域。 这项工作的大部分重点是对单个重复生物标志物和事件发生的时间进行建模。对多个重复生物标志物和事件时间数据进行联合建模是一个重要但困难的问题。在这个项目中，我们将使用频率论和贝叶斯方法来研究这个问题。 说明这种方法的重要研究包括流行病学分支的生物循环研究和预防分支的自然驱动研究。 估计月经期间关键生物标志物的模式对于了解月经期间的生物学非常重要。 此外，重要的是了解环境因素如何影响整个周期中这些生物标志物之间的关系。生物循环研究旨在解决这些科学问题。使用生物循环数据作为激励示例，我们将开发分析此类数据的新方法。 了解正常生长的范围是 DESPR 和整个研究所的一个重要研究领域。 例如，研究超声测量的正常胎儿生长范围对于识别生长异常的胎儿很重要。 对于许多这些超声纵向胎儿研究，测量的数量可能与特定胎儿的基本生长曲线有关，因为持续的随访可能基于临床决策（即，可以进行更多的超声测量）对于生长缓慢的胎儿）。 这种类型的观察机制可能会产生大量的测量结果，需要在统计建模中予以考虑。我们将开发非线性生长模型的新方法，并将其应用于胎儿生长研究的数据