Methodological issues in cardiovascular epidemiology: the risk of determining absolute risk through statistical models

Demosthenes B Panagiotakos, Vassilis StavrinosOffice of Biostatistics, Epidemiology, Department of Dietetics, Nutrition, Harokopio University, Athens, GreeceAbstract: During the past years there has been increasing interest in the development of cardiovascular disease functions that predict future e...

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Bibliographic Details
Main Authors: Demosthenes B Panagiotakos, Vassilis Stavrinos
Format: Article
Language:English
Published: Dove Medical Press 2006-09-01
Series:Vascular Health and Risk Management
Online Access:https://www.dovepress.com/methodological-issues-in-cardiovascular-epidemiology-the-risk-of-deter-peer-reviewed-article-VHRM
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Summary:Demosthenes B Panagiotakos, Vassilis StavrinosOffice of Biostatistics, Epidemiology, Department of Dietetics, Nutrition, Harokopio University, Athens, GreeceAbstract: During the past years there has been increasing interest in the development of cardiovascular disease functions that predict future events at individual level. However, this effort has not been so far very successful, since several investigators have reported large differences in the estimation of the absolute risk among different populations. For example, it seems that predictive models that have been derived from US or north European populations  overestimate the incidence of cardiovascular events in south European and Japanese populations. A potential explanation could be attributed to several factors such as geographical, cultural, social, behavioral, as well as genetic variations between the investigated populations in addition to various methodological, statistical, issues relating to the estimation of these predictive models. Based on current literature it can be concluded that, while risk prediction of future cardiovascular events is a useful tool and might be valuable in controlling the burden of the disease in a population, further work is required to improve the accuracy of the present predictive models.Keywords: cardiovascular disease, risk, models
ISSN:1178-2048