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Methods Inf Med 2014; 53(05): 371-379
DOI: 10.3414/ME13-01-0088
Original Articles
Schattauer GmbH

Combining Cross-sectional Data on Prevalence with Risk Estimates from a Prediction Model

A Novel Method for Estimating the Attributable Risk
B. Engelhardt
1   Department of Medical Biostatistics, Informatics and Epidemiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
8   Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Algorithmic Bioinformatics, Bonn, Germany
,
J. König
1   Department of Medical Biostatistics, Informatics and Epidemiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
,
M. Blettner
1   Department of Medical Biostatistics, Informatics and Epidemiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
,
P. Wild
2   Department of Medicine II, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
3   Center for Thrombosis and Hemostasis, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
,
T. Münzel
2   Department of Medicine II, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
,
K. Lackner
4   Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg- University Mainz, Mainz, Germany
,
S. Blankenberg
2   Department of Medicine II, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
5   University Heart Center Hamburg, Department of General and Interventional Cardiology, Hamburg, Germany
,
N. Pfeiffer
6   Department of Ophthalmology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
,
M. Beutel
7   Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
,
I. Zwiener
1   Department of Medical Biostatistics, Informatics and Epidemiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
› Author Affiliations
Further Information

Publication History

received:08 August 2013

accepted:10 April 2014

Publication Date:
20 January 2018 (online)

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Summary

Objectives: Estimation of the attributable risk for fatal diseases by combining two different data sources.

Methods: We derive a method to estimate the attributable risks of different risk factors by combining general mortality risks with upto-date prevalences of the risk factors using estimates from a risk prediction model and cross-sectional data of a cohort study. Partial attributable risks have been used to illustrate the proportions of the different risk factors for the attributable risk. In addition we derive standard errors for the attributable risk based on the Taylor series expansion. Since the data of our cohort study was sampled with the same size in each 10 years age stratum which does not reflect the age-structure of the general population, the attributable risk and its standard errors are calculated using an approach that allows the weighting of the data according to population proportions of age. The formula for the standard errors has been evaluated using bootstrap-techniques.

Results: We successfully implemented the method for the estimation of the attributable risk and its standard errors by integrating risk information using data of the HeartScore Germany and cross-sectional data emerging from the Gutenberg Health Study. The attributable risk can now be calculated without using the information of the overall disease rate. The bootstrap method shows, that the formula for the standard errors is useful.

Conclusion: Our method allows for the combination of different data sources in order to estimate attributable risks and our formula for the standard errors seems to yield a good approximation. But the validity of our method highly depends on the validity of the underlying data sources.

Keywords

Attributable risk - partial attributable risk - exposure - prevention - standard errors - cardiovascular diseases
 

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