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Model to assess the cost-effectiveness of new treatments for depression.

Journal article
Authors Patrik Sobocki
Mattias Ekman
Hans Ågren
Bengt Jönsson
Clas Rehnberg
Published in International journal of technology assessment in health care
Volume 22
Issue 4
Pages 469-77
ISSN 0266-4623
Publication year 2006
Published at Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
Pages 469-77
Language en
Keywords Cost-Benefit Analysis, Depression, economics, mortality, therapy, Endpoint Determination, Female, Humans, Male, Markov Chains, Middle Aged, Models, Economic, Quality-Adjusted Life Years, Sweden, Treatment Outcome
Subject categories Psychiatry


OBJECTIVES: The objective of this study was 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 with a new treatment for depression. Costs and effects were estimated for time horizons of 6 months to 5 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 with standard care, and the societal perspective was taken. Probabilistic analyses were conducted to present the uncertainty in the results, and sensitivity analyses were conducted on key parameters used 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 percent on achieving full remission, the net cost savings were 20,000 Swedish kronor over a 5-year follow-up time, given equal costs of treatments. Patients gained .073 QALYs over 5 years. The results are sensitive to changes in assigned treatment effects. CONCLUSIONS: The present study provides a new model for assessing the cost-effectiveness of treatments for depression by incorporating full remission as the treatment goal and QALYs as the primary outcome measure. Moreover, we show the usefulness of naturalistic real-life data on costs and quality of life and transition probabilities when modeling the disease over time.

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