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American Journal of Epidemiology Advance Access originally published online on April 12, 2006
American Journal of Epidemiology 2006 163(11):1042-1052; doi:10.1093/aje/kwj141
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Practice of Epidemiology

Combining Risk Estimates from Observational Studies with Different Exposure Cutpoints: A Meta-analysis on Body Mass Index and Diabetes Type 2

Nienke Hartemink1,2, Hendriek C. Boshuizen1, Nico J. D. Nagelkerke3, Monique A. M. Jacobs1 and Hans C. van Houwelingen2

1 National Institute of Public Health and the Environment, Bilthoven, the Netherlands
2 Department of Medical Statistics, Leiden University Medical Center, Leiden, the Netherlands
3 Department of Community Health, United Arab Emirates University, Al-Ain, United Arab Emirates

Correspondence to Dr. Hendriek Boshuizen, National Institute of Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, the Netherlands (e-mail: hendriek.boshuizen{at}rivm.nl).

Studies on a dose-response relation often report separate relative risks for several risk classes compared with a referent class. When performing a meta-analysis of such studies, one has to convert these relative risks into an overall relative risk for a continuous effect. Apart from taking the dependence between separate relative risks into account, this implies assigning an exposure level to each risk factor class and allowing for the nonlinearity of the dose-response relation. The authors describe a relatively simple method solving these problems. As an illustration, they applied this method in a meta-analysis of the association between body mass index and diabetes type 2, restricted to results of follow-up studies (n = 31). Results were compared with a more ad hoc method of assigning exposure levels and with a method in which the nonlinearity of the dose-response method was not taken into account. Differences with the ad hoc method were larger in studies with fewer categories. Not incorporating the nonlinearity of the dose response leads to an overestimation of the pooled relative risk, but this bias is relatively small.

body mass index; diabetes mellitus; meta-analysis; obesity


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