Clinical decisions are most secure when based on findings from several large randomized clinical trials, but relevant randomized trial data are often unavailable. Analyses using clinical data bases might provide useful information if statistical methods can adequately correct for the lack of randomization. To test this approach, the findings of the three major randomized trials of coronary bypass surgery were compared with predictions of multivariable statistical models derived from observations in the Duke Cardiovascular Disease Databank. Clinical characteristics of patients at Duke University Medical Center who met eligibility requirements for each major randomized trial were used in the models to predict 5 year survival rates expected for medical and surgical therapy in each randomized trial. Model predictions agreed well with randomized trial results and were within the 95% confidence limits of the observed survival rates in 24 (92%) of 26 clinical subgroups. The overall correlation between predicted and observed survival rates was good (Spearman coefficient 0.73, p less than 0.0001). These results suggest that carefully performed analyses of observational data can complement the results of randomized trials.