Building Disease Prediction Models by Synthesis Analysis

通过综合分析建立疾病预测模型

• 批准号: 7937087
• 负责人: XIAO-HUA A. ZHOU
• 金额: $ 22.66万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7937087
• 关键词: Adult; Blood Pressure; C-reactive protein; Cessation of life; Cholesterol; Clinical Medicine; Computer software; Coronary heart disease; Disease; Disease Outcome; Disease model; Education; Educational workshop; Event; Framingham Heart Study; Guidelines; Health; Health Promotion; Heterogeneity; Homocysteine; Homocystine; Individual; Literature; Longitudinal Studies; Malignant neoplasm of lung; Manuscripts; Measures; Medical; Meta-Analysis; Methodology; Methods; Modeling; Motion; Outcome; Persons; Publishing; Regression Analysis; Relative Risks; Reporting; Research; Research Personnel; Risk; Risk Estimate; Risk Factors; Serum Albumin; Statistical Methods; Techniques; Time; Update; base; biomedical informatics; cohort; hypercholesterolemia; programs; response; treatment planning; web site

DESCRIPTION (provided by applicant): An estimate of a person's risk for coronary heart disease (CHD) is important for many aspects of health promotion and clinical medicine. A risk prediction model on a disease outcome may be obtained through multivariate regression analysis of a longitudinal study. For example, the CHD prediction model derived from the Framingham Heart Study has been widely used and has been incorporated into the latest National Cholesterol Education Program Adult Treatment Plan III guidelines for the management of hypercholesterolemia. However, the study was started long before many currently known risk factors were suspected. Therefore, new risk factors such as serum albumin, homocysteine, C-reactive protein. A common practice of meta-analysis is combining the results of numerous studies on the effects of a risk factor on a disease outcome. If several of these composite relative risks are estimated from the medical literature for a specific disease, they cannot be combined in a multivariate risk model, as is often done in individual studies, because methods are not available to overcome the issues of risk factor colinearity and heterogeneity of the different cohorts. In this proposal, we propose new methods, called synthesis analysis, to combine different risk factors on a disease outcome from diverse published studies into multivariate models. If several composite relative risk models are available from the medical literature for a specific disease, they cannot be combined into a multivariate risk model using standard meth-analysis techniques. In this proposal, we propose new methods, called synthesis analysis, to combine different risk factors on a disease outcome from diverse published studies into multivariate models.

描述（由申请人提供）：对一个人患冠状动脉疾病（CHD）风险的估计对于健康促进和临床医学的许多方面都很重要。可以通过纵向研究的多元回归分析获得有关疾病结果的风险预测模型。例如，从弗雷明汉心脏研究中得出的CHD预测模型已被广泛使用，并已纳入了最新的国家胆固醇教育计划成人治疗计划III指南，用于管理高胆固醇血症。但是，这项研究很早就开始了，这是在怀疑许多当前已知的危险因素之前。因此，新的危险因素，例如血清白蛋白，同型半胱氨酸，C反应蛋白。荟萃分析的一种常见实践是结合了许多研究危险因素对疾病结果的影响的结果。如果从医学文献中估算出特定疾病的一些综合相对风险，则不能像单个研究中那样在多元风险模型中合并，因为无法克服不同同类群体的危险因素分子性和异质性问题。在此提案中，我们提出了称为合成分析的新方法，以结合不同风险因素，从不同的已发表研究到多元模型的疾病结果。如果医学文献可从医学文献获得特定疾病的几种复合相对风险模型，则不能使用标准的甲基分析技术将它们合并为多元风险模型。在此提案中，我们提出了称为合成分析的新方法，以结合不同风险因素，从不同的已发表研究到多元模型的疾病结果。