ASSUMING INDEPENDENCE OF RISK FACTOR PREVALENCES IN SIMULATION-MODELSLIKE PREVENT - WHEN ARE THE OUTCOMES SERIOUSLY BIASED

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 m odels like PREVENT, designed to calculate the health benefits of a cha nge in risk factor prevalences, is computed assuming an independent di stribution. This assumption may not be valid. The aim of the present s tudy was to quantify the maximum extent to which outcome measures of P REVENT 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 ind ependent 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 est imate of how serious the bias might be. Furthermore, sensitivity analy ses were carried out to explore the extent of bias in the case of diff erent risk factor prevalences. The results show that the assumed indep endence has little impact on the estimated life expectancy and the pot ential years of life gained, both in the case of the current risk fact or prevalences and in the case of higher or lower prevalences. Given t hat the dependency between risk factors will probably be smaller in re ality, we conclude that the assumption of independence may be used sin ce it is not likely to cause substantial bias. This greatly reduces th e data requirements necessary as input for simulation models such as P REVENT.