Objectives: To examine the generalisability of multivariate risk functions from diverse populations in three contexts: ordering risk, magnitude of relative risks, and estimation of absolute risk. Design: Meta-analysis of prospective cohort studies. Patients: Participants from various epidemiological studies. Main outcome measure: Death from coronary heart disease (CHD). Results: The analysis included 105 420 men and 56 535 women 35-74 years of age and free of CHD at baseline from 16 observational studies with a total of 27 analytical groups. The area under the receiver operating characteristic curve (AUC) was used to judge the ability of the multivariate risk function to order risk correctly. AUCs ranged from 0.60 to 0.80. The AUCs differed significantly between the studies (p < 0.01) but were very similar for different risk functions applied to the same population, indicating similar ability to rank risk for different models. The magnitudes of the relative risks associated with major risk factors (age, systolic blood pressure, serum total cholesterol, smoking, and diabetes) varied significantly across studies (p < 0.05 for homogeneity). The prediction of absolute risk was not very accurate in most of the cases when a model derived from one study was applied to a different study. Conclusions: When considered qualitatively, the major risk factors are associated with CHD mortality in a diverse set of populations. However, when considered quantitatively, there was significant heterogeneity in all three aspects: ordering risk, magnitude of relative risks, and estimation of absolute risk. C oronary heart disease (CHD) is a leading cause of death in many countries. Prospective studies around the world have identified major risk factors for developing CHD and, based on these risk factors, functions have been developed to predict the occurrence of CHD in individual patients. Although many researchers have examined whether a risk function based on a single population is valid when applied to other populations, 1-12 most have involved a small number of studies and the various aspects of predictive accuracy have not been systematically examined. In this report we examine the predictive accuracy of risk functions using three successively stronger sets of criterion: ordering risk, estimating relative risk, and estimating absolute risk.The lowest level of validity for a predictive function is its ability to rank individual patients within a population according to their risk, differentiating patients with higher risk from those at lower risk levels. The absolute level of risk is not a concern; only the ordering is important. Several reports have examined the ability of a single risk function to order risk across studies and judged the validity of the risk function by this criterion. Estimating relative risk is more difficult than ordering. A more stringent criterion would require that the model parameters relating risk factors to disease be the same in different populations. Comparisons in the literature based on this criteri...

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