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Epidemiological isk assessment based on structural equations with latent variables

基于隐变量结构方程的流行病学风险评估

基本信息

批准号：
09470119
负责人：
YOSHIDA Katsumi
金额：
$ 2.24万
依托单位：
St. Marianna University School of Medicine
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1997
资助国家：
日本
起止时间：
1997 至 1998
项目状态：
已结题

项目摘要

Risk assessment has attracted much interest according to the importance of primary health care. Risk assessment is usually based on the knowledge acquisition from epidemiological database. Statistical analysis has been standard method for getting knowledge from database. However, this kind of analysis seems to be not enough appropriate to evaluate the non-linear conditions. This project aims to introduce the structural equations with latent variables to epidemiological risk assessment in the non-linear conditions.The epidemiological data was gathered from the TOYAMA study(cohort study of children in Toyama Prefecture). The risk was calculated for the occurrence of the obese child in the elementary school from 3 y.o. normal body composition. The results were compared between structural equations with latent variables, logistic model and neural network model. The structural equation model showed the advantage of effective analysis by using the latent variables. The models were evaluated based on goodness of fit index, adjusted goodness of fit index, Akaike's information criterion. The results indicated the latent variables should be incorporated into the equations as the products of observed variables. Structural equations should be also evaluated from the viewpoints of medical causal relationships.This structural equations showed enough abilities to express the non-linear relationships and the expertise. However, estimation of individual risk was difficuit in the structural equations compared with logistic and neural network models.

由于初级卫生保健的重要性，风险评估引起了人们的极大兴趣。风险评估通常基于从流行病学数据库中获取的知识。统计分析已经成为从数据库中获取知识的标准方法。然而，这种分析似乎不足以适当地评估非线性条件。本研究目的是将隐变量结构方程引入非线性条件下的流行病学风险评估中，研究资料来自TOYAMA研究（富山县儿童队列研究）。从3岁开始计算小学生肥胖发生的危险度。正常的身体组成。比较了隐变量结构方程、Logistic模型和神经网络模型的预测结果。结构方程模型利用隐变量进行有效的分析，体现了其优势。采用拟合优度指数、调整拟合优度指数、赤池信息量准则对模型进行评价。结果表明，潜变量应作为观测变量的乘积纳入方程。从医学因果关系的角度对结构方程进行评价，该结构方程表现出了足够的表达非线性关系和专业知识的能力。然而，与Logistic和神经网络模型相比，结构方程难以估计个体风险。