A discrete-choice experiment to assess treatment modality preferences of patients with depression

Aims: There is an increasing interest in understanding patients' preferences in the area of healthcare decision-making to better match treatment with patients' preferences and improve treatment uptake and adherence. The aim of this study was to elicit the preferences of patients with a depressive disorder regarding treatment modalities. Materials and methods: In a discrete-choice experiment, patients chose repetitively between two hypothetical depression treatments that varied in four treatment attributes: waiting time until the start of treatment, treatment intensity, level of digitalization, and group size. A Bayesian-efficient design was used to develop 12 choice sets, and patients' preferences and preference variation was estimated using a random parameters logit model. Results: A total of 165 patients with depression completed the survey. Patients preferred short (over long) waiting times, face-to-face (over digital) treatment, individual (over group) treatment, and one session per week over two sessions per week or one session per 2 weeks. Patients disfavoured digital treatment and treatment in a large group. Waiting time and treatment intensity were substantially less important attributes to patients than face-to-face (vs digital) and group size. Significant variation in preferences was observed for each attribute, and sub-group analyses revealed that these differences were in part related to education. Limitations: The convenience sample over-represented the female and younger population, limiting generalizability. Limited information on background characteristics limited the possibilities to explore preference heterogeneity. Conclusion: This study demonstrated how different treatment components for depression affect patients' preferences for those treatments. There is significant variation in treatment preferences, even after accounting for education. Incorporating individual patients' preferences into treatment decisions could potentially lead to improved adherence of treatments for depressive disorders.