REGRESSION FOR TIME-DEPENDENT DATA

时变数据的回归

• 批准号: 3454450
• 负责人: ELLEN J. MACKENZIE
• 金额: $ 11.24万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-02-01 至 1993-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3454450
• 关键词: epidemiology; longitudinal human study; mathematical model; model design /development; statistics /biometry; time resolved data

A common problem in biomedical research is to describe a response (Y) as a function of explanatory variables (X). When observations are independent, generalized linear models (GLM's) including linear, logistic, log-linear and proportional hazards models are well developed for this purpose. When observations are correlated, current techniques are inadequate, particularly for discrete and non-normal outcomes. This study will develop regression methods for correlated data which arise in longitudinal and time series studies. In longitudinal studies, a few observations are made on each of many subjects. Repeated valves for an individual are correlated. In time series studies, inferences about the relationship of Y and X are made from a single longer set of correlated observations. By developing extensions of GLM's, the methods will be useful for a variety of discrete and continuous responses common to biomedical and particularly infectious disease research. For longitudinal studies, I will develop transitional, marginal and random effects (mixed) models. These will be used to analyze longitudinal data from the Multicenter AIDS Cohort Study (MACS), the Haiti HIV Study (HHS) and a study of infectious diseases and Vitamin A deficiency in Indonesian children (ICS). For the time series case, parameter and observation driven models (Cox, 1981) will be studied. These models will be used to estimate trends and make short-term projections of AIDS incidence by risk group and state in the U.S. and of hepatitis incidence by county in the State of Pennsylvania.

生物医学研究中的一个常见问题是描述一种 响应(Y)作为解释变量(X)的函数。什么时候 观测值是独立的广义线性模型(GLM) 包括线性、逻辑、对数线性和比例风险 为此目的而开发的模型都很好。当观察到 是相关的，目前的技术是不够的，特别是对于 离散的和非正常的结果。这项研究将得到进一步发展 纵向相关数据的回归方法 和时间序列研究。在纵向研究中，有几项 对许多主题的每一个都进行了观察。重复瓣膜 对于个人来说是相互关联的。在时间序列研究中，推论 关于Y和X的关系是由一个单独的较长的 一组相关的观测数据。 通过开发GLM的扩展，这些方法将对 常见的各种离散和连续响应 生物医学，特别是传染病研究。为 纵向研究，我将发展过渡性、边际性和 随机效应(混合)模型。这些将被用来分析 多中心艾滋病队列研究的纵向数据 (MACS)、海地艾滋病毒研究(HHS)和一项传染性疾病研究 印度尼西亚儿童中的疾病和维生素A缺乏(ICS)。 对于时间序列情况，参数和观测驱动 模型(COX，1981)将被研究。这些模型将用于 估计艾滋病的趋势并作出短期预测 美国按风险群体和州划分的发病率和肝炎 宾夕法尼亚州按县统计的发病率。