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）：评估一个人患冠心病 (CHD) 的风险对于健康促进和临床医学的许多方面都很重要。疾病结果的风险预测模型可以通过纵向研究的多元回归分析来获得。例如，源自弗雷明汉心脏研究的冠心病预测模型已得到广泛应用，并已纳入最新的国家胆固醇教育计划成人治疗计划III高胆固醇血症管理指南。然而，这项研究早在许多目前已知的危险因素被怀疑之前就开始了。因此，新的危险因素如血清白蛋白、同型半胱氨酸、C反应蛋白。荟萃分析的常见做法是结合大量关于危险因素对疾病结果影响的研究结果。如果从特定疾病的医学文献中估计其中的几个复合相对风险，则不能像个体研究中经常所做的那样将它们组合到多变量风险模型中，因为没有方法可以克服不同群体的风险因素共线性和异质性问题。在本提案中，我们提出了称为综合分析的新方法，将来自不同已发表研究的疾病结果的不同风险因素结合到多变量模型中。如果可以从医学文献中针对特定疾病获得多个复合相对风险模型，则无法使用标准方法分析技术将它们组合成多变量风险模型。在本提案中，我们提出了称为综合分析的新方法，将来自不同已发表研究的疾病结果的不同风险因素结合到多变量模型中。