Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual–participant data meta-analysis

Background: There is limited evidence on the cost effectiveness of Internet-based treatments for depression. The aim was to evaluate the cost effectiveness of guided Internet-based interventions for depression compared to controls. Methods: Individual–participant data from five randomized controlled trials (RCT), including 1,426 participants, were combined. Cost-effectiveness analyses were conducted at 8 weeks, 6 months, and 12 months follow-up. Results: The guided Internet-based interventions were more costly than the controls, but not statistically significant (12 months mean difference = €406, 95% CI: − 611 to 1,444). The mean differences in clinical effects were not statistically significant (12 months mean difference = 1.75, 95% CI: −.09 to 3.60 in Center for Epidemiologic Studies Depression Scale [CES-D] score,.06, 95% CI: −.02 to.13 in response rate, and.00, 95% CI: −.03 to.03 in quality-adjusted life-years [QALYs]). Cost-effectiveness acceptability curves indicated that high investments are needed to reach an acceptable probability that the intervention is cost effective compared to control for CES-D and response to treatment (e.g., at 12-month follow-up the probability of being cost effective was.95 at a ceiling ratio of 2,000 €/point of improvement in CES-D score). For QALYs, the intervention's probability of being cost effective compared to control was low at the commonly accepted willingness-to-pay threshold (e.g., at 12-month follow-up the probability was.29 and. 31 at a ceiling ratio of 24,000 and 35,000 €/QALY, respectively). Conclusions: Based on the present findings, guided Internet-based interventions for depression are not considered cost effective compared to controls. However, only a minority of RCTs investigating the clinical effectiveness of guided Internet-based interventions also assessed cost effectiveness and were included in this individual–participant data meta-analysis.