Statistical Methods For Longitudinal And Other Studies

纵向研究和其他研究的统计方法

基本信息

批准号：
8149253
负责人：
Kai F Yu
金额：
$ 23.39万
依托单位：
EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
依托单位国家：
美国
项目类别：
财政年份：
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/8149253
关键词：
Statistical Methods

项目摘要

This project will investigate theory, methods and applications of mathematical statistics and probability, with particular emphasis on the problems with data collected by NICHD. Current focus is on 1) the analysis of data arising from longitudinal studies with repeated measurements, 2)nonparametric procedures, 3) likelihood approaches to nonparametric two-sample problem for right-censored data, 4) sequential clincial trials, and 5) general methodology for reproductive and perinatal epidemiology. Examples of NICHD projects on longitudinal studies are Successive Small-for-Gestational Age Study I and Study II in Alabama and Scandinavia, and the Longitudinal Study of Vaginal Flora. A host of statistical procedures for estimation and hypothesis testing will be proposed and investigated for the time varying coefficient models via their asymptotic properties and simulations. Applications will be developed to handle questions concerning various issues in perinatal and reproductive epidemiology. New and rigorous statistical methods and algorithms will be generated and validated through investigation of their statistical and probabilistic properties. Computer-intensive techniques such as bootstrapping methodology will be investigated for the relevant problems. Among the applications of the developed methodology are fetal growth, maternal risk factors and pregnancy outcomes. Regression models for unbalanced longitudinal ordinal data will be studied. Major motivation and application come from the Longitudinal Study of Vaginal Flora. One direction is to develop sequential methodologies for clinical trials. Particular focus will be on the estimation problems following the termination of a clinical trial. Adaptive designs in clinical trials will be studied. Also under investigation is the incorporation of partial overrruning into the final analysis of a sequential clinical trial. Longitudinal analysis for discrete data and sequential adaptive designs will be the major focus for the near future. Point and interval estimation for two-stage adaptive procedures will be studied. A two-stage adaptive procdeure will be designed for the selection of the best diagnostic biomarker. General linear mixed models are very broad and constitute an important class of statistical models for longitudinal studies in many areas of biomedical and epidemiological studies. The statistical procedures and properties thereof are numerous. We carve out a small area for more detailed and deeper investigation. For the general linear mixed models, including some special cases, the least squares estimators and the analysis of variance estimators of the fixed effects and the variance are studied. Necessary and sufficient conditions are derived for the simultaneous optimality of both types of estimators. Variance components for the general linear mixed effects models are studied. A comparison of the analysis of variance estimators and the estimators under spectral decomposition is investigated. Analysis of variance statistical procedures for the general linear mixed models under heteroscedasticity will be investigated. Parametric and semi-parametric regression models are used to evaluate the change of the mean response over time and the effects of the explanatory variables of interest on the mean response. Because of their sensitivity to model specifications, inadequate parametric and semi-parametric modeling may lead to erroneous conclusions. A global goodness of fit test is to assess the adequacy of a chosen parametric or semiparametric model versus a more general nonparametric varying coefficient model. The test statistics based on the squared distances of the smoothed residuals under the parametric or semi-parametric model, have asymptotically normal distributions under the null hypotheses as well as under the alternatives. Approximate critical values and the power functions of the tests can be derived from the asymptotic distributions. Finite sample properties of our test procedures are investigated through a Monte Carlo simulation. Application of our test procedure is demonstrated through the Small-for-Gestational Age Study in Alabama. This goodness of fit tests can also be applied to other data sets. In general growth studies including fetal growth, usually a unit is under observation and a measurement of a certain attribute is made repeatedly at successive times. The data collected will consist of the growth measurements and the corresponding growth times. There are situations when the growth times are not completely known to the investigators when the measurements are made. However, the inter-measurement times are always known. The investigation of the growth dynamics without the benefit of the complete knowledge of the growth times is principally motivated by the Small-for-Gestational Age Study in Alabama. Ultrasound measurements on the fetuses are made at various times of the fetal growth. When the measurements are made, the growth times (gestational ages) are at best estimated; however the times elapsed between measurements are completely determined. There is a spectrum of different levels of knowledge about the growth times. They can be (a) completely unknown, (b) completely known, and (c) partially known, an example being that there is also an instrument to measure the growth time but it is not accurate enough to render reliable readings. Appropriate parametric models are used to describe the growth dynamics of the fetus. Different levels of knowledge of growth times will be considered. We propose an approach of hypothesis testing which bridges the classical likelihood ratio test and the fully Bayes factor. Statistical properties of the approach will be examined in terms of classical criteria. In particular we shall examine when it will reduce to the classical case and when it will give a fully Bayes factor. Under some conditions, the significance level can be controlled. We explore statistical models, including transition models, to describe the history of bacterial vaginosis based on the data base from the Longitudinal Study of Vaginal Flora and the risk factors.

该项目将研究数学统计和概率的理论，方法和应用，并特别强调NICHD收集的数据问题。目前的重点是1）通过重复测量的纵向研究产生的数据分析，2）非参数程序，3）对于右手数据的非参数两样本问题的可能性方法，4）序列层clincial层试验和5）5）用于生殖性和截肢性和围产期流行病学的一般方法。纵向研究的NICHD项目的例子是在阿拉巴马州和斯堪的纳维亚半岛连续的胎龄研究I和研究II，以及阴道菌群的纵向研究。将通过其渐近性能和模拟来提出大量用于估计和假设检验的统计程序。将开发应用程序来处理有关围产期和生殖流行病学各种问题的问题。通过研究其统计和概率特性，将生成和验证新的和严格的统计方法和算法。将针对相关问题研究计算机密集型技术，例如引导方法。在开发方法的应用中，有胎儿的生长，母体危险因素和妊娠结局。将研究不平衡纵向序数数据的回归模型。主要动机和应用来自阴道菌群的纵向研究。一个方向是开发用于临床试验的顺序方法。临床试验终止后，特别的重点将放在估计问题上。将研究临床试验中的自适应设计。还在研究的是，将部分过度缩入纳入顺序临床试验的最终分析中。离散数据和顺序自适应设计的纵向分析将是不久的将来的主要重点。将研究两阶段自适应程序的点和间隔估计。将设计一个两阶段的自适应procdeure，以选择最佳的诊断生物标志物。通用线性混合模型非常广泛，构成了许多生物医学和流行病学研究领域的纵向研究的重要统计模型。统计程序及其属性很多。我们挖出一个小区域，以进行更详细和更深入的研究。对于一般线性混合模型，包括一些特殊情况，研究了最小二乘估计器以及固定效应和方差方差估计器的分析。为两种类型的估计器的同时最优性提供了必要和足够的条件。研究了通用线性混合效应模型的方差成分。研究了方差估计器和光谱分解下的估计值的比较。将研究在异方差下对通用线性混合模型的方差统计程序的分析。参数和半参数回归模型用于评估一段时间内平均响应的变化以及感兴趣的解释变量对平均响应的影响。由于它们对模型规范的敏感性，参数和半参数建模不足可能会导致错误的结论。拟合测试的全球优点是评估所选参数或半摩托模型与更通用的非参数变化系数模型的充分性。基于参数或半参数模型下平滑残差的平方距离的测试统计量具有渐近的正态分布，在零假设以及替代方案下。近似临界值和测试的功率函数可以从渐近分布中得出。通过蒙特卡洛模拟研究了我们的测试程序的有限样品特性。通过阿拉巴马州的小胎龄研究证明了我们的测试程序的应用。适合测试的这种优点也可以应用于其他数据集。在包括胎儿生长在内的一般生长研究中，通常正在观察一个单元，并且在连续的时间反复进行某个属性的测量。收集的数据将包括增长测量值和相应的增长时间。在进行测量时，在某些情况下，研究人员并不完全知道生长时间。但是，互估的时间始终是已知的。对生长动态的调查没有使生长时间完全了解的利益，主要是由于阿拉巴马州的小胎龄研究所激发的。胎儿的超声测量是在胎儿生长的不同时间进行的。进行测量时，充其量的增长时间（妊娠年龄）。但是，完全确定了测量之间经过的时间。关于增长时间的知识水平不同。它们可以是（a）完全未知的，（b）完全知道，（c）部分知道的例子是，还有一种仪器来测量增长时间，但不够准确，无法呈现可靠的读数。适当的参数模型用于描述胎儿的生长动力学。将考虑不同水平的增长时间知识。我们提出了一种假设检验的方法，该方法桥接了经典的可能性比测试和完全贝叶斯因子。该方法的统计特性将根据经典标准进行检查。特别是我们将检查何时将其减少到经典案例，以及何时给出完全贝叶斯的因素。在某些情况下，可以控制显着性水平。我们探讨了包括过渡模型在内的统计模型，以根据阴道菌群纵向研究和危险因素的数据库来描述细菌性阴道病的历史。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Estimation of Variance Components in the Mixed-Effects Models: A Comparison Between Analysis of Variance and Spectral Decomposition.

混合效应模型中方差分量的估计：方差分析与谱分解之间的比较。

DOI：
10.1016/j.jspi.2009.03.014
发表时间：
2009
期刊：
Journal of statistical planning and inference
影响因子：
0.9
作者：
Wu,Mi-Xia;Yu,Kai-Fun;Liu,Ai-Yi
通讯作者：
Liu,Ai-Yi

Exact inference on contrasts in means of intraclass correlation models with missing responses.

DOI：
10.1016/j.jmva.2008.05.002
发表时间：
2009-02-01
期刊：
JOURNAL OF MULTIVARIATE ANALYSIS
影响因子：
1.6
作者：
Wu, Mi-Xia;Yu, Kai F.;Liu, Aiyi
通讯作者：
Liu, Aiyi

Two-stage procedures for selecting the best diagnostic biomarkers.

选择最佳诊断生物标志物的两阶段程序。

DOI：
10.1098/rsta.2008.0032
发表时间：
2008
期刊：
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
影响因子：
0
作者：
Liu,Aiyi;Wu,Chengqing;Yu,KaiF
通讯作者：
Yu,KaiF

A threshold sample-enrichment approach in a clinical trial with heterogeneous subpopulations.