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The European Journal of Public Health 1997 7(2):216-220; doi:10.1093/eurpub/7.2.216
© 1997 by European Journal of Public Health
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METHODS

Assuming independence of risk factor prevalences in simulation models like PREVENT

When are the outcomes seriously biased?

PERLA J. VAN DE MHEEN and LOUISE J. GUNNING-SCHEPERS

Academic Medical Center, Institute of Social Medicine Amsterdam, The Netherlands

Ms. Perla J. van de Mheen, PhD. Academic Medical Center, Institute of Social Medicine, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands, tel. {boxplus}31 20 5664892, fax {boxplus}31 20 6972316

Little is known about the clustering of risk factors at a nation-wide level. As a result the prevalence of combinations of risk factors in models like PREVENT, designed to calculate the health benefits of a change in risk factor prevalences, is computed assuming an independent distribution. This assumption may not be valid. The aim of the present study was to quantify the maximum extent to which outcome measures of PREVENT may be biased, if the assumed independent distribution of risk factors is incorrect. We therefore calculated to what extent the life expectancy and the potential years of life gained were biased when independent risk factor prevalences were assumed, while they were in fact completely dependent. We used population data, mortality figures and risk factor prevalences from The Netherlands to obtain a realistic estimate of how serious the bias might be. Furthermore, sensitivity analyses were carried out to explore the extent of bias in the case of different risk factor prevalences. The results show that the assumed independence has little impact on the estimated life expectancy and the potential years of life gained, both in the case of the current risk factor prevalences and in the case of higher or lower prevalences. Given that the dependency between risk factors will probably be smaller in reality, we conclude that the assumption of independence may be used since it is not likely to cause substantial bias. This greatly reduces the data requirements necessary as input for simulation models such as PREVENT.

Keywords: clustering, independent risk factors, simulation models, life expectancy, years of life gained


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