Background. Patient decision aids (PtDAs) supplement advice from health care professionals by communicating the absolute risk or benefit of treatment options (i.e., X/100). As such, PtDAs have been amenable to binary outcomes only. We aimed to develop and test the validity of the Conversion to Risk Estimates through Application of Normal Theory (CREATE) method for estimating absolute risk based on continuous outcome data. Methods. CREATE is designed to derive an estimate of the proportion of those who experience a clinically relevant degree of change (CRDoC). We used a 2-stage validation process using real and simulated change score data, respectively. First, using raw data from published intervention trials, we calculated the proportion of patients with a CRDoC and compared that with our CREATE-derived estimate using chi-square tests of association. Second, 200,000 simulated distributions of change scores were generated with widely varying distribution characteristics. Actual and CREATE-derived estimates were compared for each simulated distribution, and relative differences were summarized graphically. Results. The absolute difference between the estimated and actual CRDoC did not exceed 5% for any of the samples based on real data. Applying the CREATE method to 200,000 simulated scenarios demonstrated that the CREATE method should be avoided for outcomes where the underlying distribution can be reasonably assumed to have high levels of skew or kurtosis. Conclusion. Our results suggest that standard statistical theory can be used to estimate continuous outcomes in absolute terms with reasonable accuracy for use in PtDAs; caution is advised if outcome summary statistics are assumed to have been derived from highly skewed distributions.