Evidence on the cost-effectiveness of healthcare interventions is increasingly required by decision-makers. Economic models can provide timely information on the long-term impact of new technologies. However, models have been criticised because of the implicit assumptions they make, in particular the methods used to extrapolate data are rarely documented. This paper presents a systematic process for choosing a method of predicting events in economic models. This process is illustrated using a model examining the cost-effectiveness of a new HMG-CoA reductase inhibitor (statin) for primary prevention of cardiovascular disease (CVD). The prediction of future CVD events is a central component of the model, and the choice of method for predicting events was an important issue in the model's development. A literature review identified 11 studies with the information required to predict CVD events. A set of criteria were developed to assess the different methods of risk estimation, covering issues like scientific validity and acceptability to decision-makers. Risk equations derived from the Framingham Heart Study were found to be most suitable for predicting future events in the economic model. The paper illustrates how the development of economic models can be made more transparent, and suggests that the process outlined may be applied to other disease areas where there are several event prediction methods to choose from. In disease areas where published methods for predicting events are not available, the process outlined can make the uncertainty this leads to explicit, and highlight where further research is required. Such transparency can help decision-makers understand the scientific basis underpinning models, and therefore make these models more acceptable and useful for health policy-making.