OBJECTIVES: To develop a model to assess the cost-effectiveness of a new treatment for patients with depression. METHODS: A Markov simulation model was constructed to evaluate standard care for depression as performed in clinical practice compared to a new treatment for depression. Costs and effects were estimated for time horizons of six months to five years. A naturalistic longitudinal observational study provided data on costs, quality of life and transition probabilities. Data on long-term consequences of depression and mortality risks were collected from the literature. Cost-effectiveness was quantified as quality-adjusted life years (QALYs) gained from the new treatment compared to standard care and the societal perspective was taken. Probabilistic analyses were conducted in order to present the uncertainty in the results and sensitivity analyses were conducted on key parameters employed in the model. RESULTS: Compared with standard care, the new hypothetical therapy was predicted to substantially decrease costs and was also associated with gains in QALYs. With an improved treatment effect of 50% on achieving full remission the net cost savings were 20,000 Swedish kronor (SEK) over a follow-up time of 5 years, given equal costs of treatments. Patients gained 0.073 QALYs over 5 years. The results are sensitive to changes in assigned treatment effects. CONCLUSION: The present paper provides a new approach for assessing the cost-effectiveness of treatments for depression by incorporating full remission as treatment goal and QALYs as primary outcome measure. Moreover, we show the usefulness of naturalistic real-life data on costs and quality of life and transition probabilities when modelling the disease over time.