The choice of data used in decision modeling of health care interventions divides analysts into two groups: those who favor randomized clinical trial (RCT) data and those who prefer "real world" data. This decision may have serious consequences if the end result is to inform health care policy. This workshop employs a case study to (1) show how differences in the reality of clinical practice and the rigor of RCTs can lead to biases when decision models use RCT data to evaluate policy issues and (2) provide a method of updating decision models with claims/outcomes data to overcome this bias. We highlight three specific problems associated with the use of RCT data which may create misleading results: randomization and sample selection bias, clinically appropriate comparator groups, and indirect treatment effects. These issues are illustrated with a decision model analyzing Medicare's coverage of erythropoietin (EPO) for patients with End-Stage Renal Disease (ESRD). We show how logistic and multiple regression can be used to estimate branch probabilities and payoffs for each treatment group. The incorporation of additional data from the United States Renal Data System into the model enables us to update probabilities and payoffs when patients are not randomly assigned to treatment modalities. To highlight the potential bias that exists when models rely solely on RCT data, we compare our results to a previous study in which the authors employed a computerized decision model to estimate the net costs to Medicare of EPO coverage at 1- and 5-years. This exercise will offer policy analysts and others a method of updating RCT-based decision models to more accurately reflect clinical practice and predict policy effects.