alexa Revisiting the Concentration Curves and Indices as Useful Tools for Assessing Relative and Attributable Risks | OMICS International | Abstract
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
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Research Article

Revisiting the Concentration Curves and Indices as Useful Tools for Assessing Relative and Attributable Risks

Yuejen Zhao1* and Andy H. Lee2

1Adjunct Senior Research Fellow, Institute of Advanced Studies, Charles Darwin University, Darwin, NT, Australia

2Professor of Biostatistics, School of Public Health, Curtin University, Australia

*Corresponding Author:
Yuejen Zhao
Adjunct Senior Research Fellow
Institute of Advanced Studies, Charles Darwin University
Principal Health EconomistDepartment of Health,Darwin Plaza
Level 1,41 Smith St, Darwin, NT 0800, Australia
Tel: 61-8-89858077
Fax: 61-8-89858075
E-mail: [email protected]

Received date: March 06, 2012; Accepted date: July 20, 2012; Published date: July 25, 2012

Citation: Zhao Y, Lee AH (2012) Revisiting the Concentration Curves and Indices as Useful Tools for Assessing Relative and Attributable Risks. J Biomet Biostat S7-019.doi: 10.4172/2155-6180.S7-019

Copyright: © 2012 Zhao Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Accurate assessment of the association between exposure and response is central to identifying causality in medical research. The concentration index has been commonly used to study income inequality and socioeconomic related health inequality. This study generalizes applications of the concentration index to measure the relative and attributable risks for describing exposure-response relationships in medical research. Based on cumulative distribution functions, a new measure of correlation is proposed to quantify the association between exposure and response. The
connection between the new and existing measures is discussed. The method enables the semi-parametric analysis of overall association and disparity by risk factors. Both grouped and continuous data situations are considered with two applications. The first example illustrates the relationships between the concentration index, relative and attributable risks. The second example demonstrates how the concentration index can assist in evaluating the association between the radiation dose and the incidence of leukaemia. Logistic regression based decomposition is compared with the new
approach. We found the concentration index analysis useful not only for examining socioeconomic determinants of health, but also for assessing quantitative relations between exposures to health risks and ill-health outcomes.

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