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大多数流行病学，统计和预防研究（DESPR）的研究以及整个NICHD的研究很重要。 在许多领域中，需要进行其他方法论研究以适当分析研究中的数据。 当分析纵向研究数据时，缺少数据是一个常见的问题。研究人员在分析其研究数据时需要考虑这些丢失的数据。 开发用于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。尽管单个纵向测量有广泛的文献，但是当纵向测量数量较大时，几乎没有适当地考虑数据的工作。 我们将开发在该项目下解决此问题的新方法。 开发共同建模事件和重复测量的方法目前是生物统计研究的活跃领域。 这项工作的大部分都集中在建模单个重复的生物标志物和活动的时间。共同建模多个重复的生物标志物和事件数据的时间是一个重要但困难的问题。在这个项目中，我们将通过常见主义者和贝叶斯方法研究这个问题。 将说明该方法的重要研究包括流行病学分支中的杀菌研究和预防分支中的自然驾驶研究。 在月经期估计关键生物标志物中的模式对于理解月经时期的生物学很重要。 同样，重要的是了解环境因素如何影响周期中这些生物标志物之间的关系。生物周期研究旨在解决这些科学问题。使用生物周期数据作为一个激励示例，我们将开发分析此类数据的新方法。 了解正常增长的范围是DESPR和整个研究所内的重要研究领域。 例如，研究正常胎儿生长的范围通过超声波测量对于识别异常生长的胎儿很重要。 对于这些超声纵向胎儿研究中的许多，测量的数量可能与特定胎儿的潜在生长曲线有关，因为持续的随访可能是基于临床决策（即，可能对生长缓慢生长的胎儿进行更多的超声测量）。 这种类型的观察机制可能会导致需要在统计建模中考虑到需要考虑的测量数量。我们将开发非线性增长模型的新方法，并将其应用于我们胎儿生长研究的数据