权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

MODEL FREE TIME CURVES FOR LONGITUDINAL DATA ANALYSIS

用于纵向数据分析的自由时间曲线模型

基本信息

批准号：
3356401
负责人：
P DAVID WILSON
金额：
$ 5.64万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
1991
资助国家：
美国
起止时间：
1991-01-01 至 1994-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/3356401
关键词：
computer data analysis computer program /software computer simulation epidemiology human data longitudinal human study mathematical model model design /development statistics /biometry

项目摘要

In previous work the investigator has developed the Disturbed Highest Derivative Polynomial (DHDP) as a model-free time curve and has published the theoretical development for its use as the overall time curve in a linear Gaussian model for longitudinal data with fixed covariate effects and autocorrelated errors but without subject effects. The aim of this proposal is to enhance analysis statistical methods for epidemiological studies by extending this development to models for binary-logistic and Poisson data and by including random subject effects in the Gaussian model. For the logistic model, the DHDP would replace the constant which appears in the log odds in the non-longitudinal case. The first-order DHDP is a straight line whose slope receives random disturbances over time. As such it capable of fitting a rich variety of arbitrarily changing time curves. The second-order DHDP would generally provide a fit with smaller high frequency variation. There are a number of longitudinal data analysis methods currently available for Gaussian and binary-logistic data. They all have in common the requirement to explicitly model the overall time curve--usually by a low order deterministic polynomial. The main significance of this proposal will be to represent the overall time curve by a DHDP, thereby allowing the possibility for fitting arbitrarily changing time curves without explicitly modeling the form of the change over time. The order of the DHDP can be selected by a modification of the Akaike Information Criterion. The Poisson model will be useful in fitting the periodic reported incidence of a rare disease. The relationship of the DHDP to the Smoothing Polynomial Spline (SPS) will be shown and methods will be developed for using a SPS instead of a DHDP in analysis. Robustness of the methods will be examined by computer simulation studies which will evaluate and compare the ability of the DHDP and SPS models to estimate covariate effects and time curves when the time curves are generated by processes other than DHDP.

在之前的工作中，调查员开发了干扰最高值导数多项式（DHDP）作为无模型时间曲线，并已发表将其用作总体时间曲线的理论发展具有固定协变量效应的纵向数据的线性高斯模型和自相关误差，但没有主题效应。此举的目的建议加强流行病学分析统计方法通过将这种发展扩展到二元逻辑模型和泊松数据并在高斯模型中包含随机主体效应。对于逻辑模型，DHDP 将替换出现的常数在非纵向情况下的对数赔率。一阶 DHDP 是其斜率随时间受到随机干扰的直线。像这样它能够拟合丰富多样的任意变化的时间曲线。二阶 DHDP 通常会提供较小高的拟合频率变化。有多项纵向数据分析目前可用于高斯和二元逻辑数据的方法。他们所有这些都有一个共同点，即要求对总体时间进行显式建模曲线——通常由低阶确定性多项式表示。主要该提案的意义在于代表总体时间曲线通过 DHDP，从而允许任意拟合的可能性改变时间曲线而不明确建模变化的形式随着时间的推移。 DHDP的顺序可以通过修改来选择赤池信息准则。泊松模型将有助于拟合定期报告的罕见疾病的发病率。的关系将展示 DHDP 到平滑多项式样条 (SPS) 的方法将开发用于在分析中使用 SPS 代替 DHDP。该方法的稳健性将通过计算机模拟研究进行检验它将评估和比较 DHDP 和 SPS 模型的能力当时间曲线为由 DHCP 以外的进程生成。