A Promising Approach to Optimizing Sequential Treatment Decisions for Depression: Markov Decision Process

Area of Science:

• Decision Analysis
• Health Economics
• Computational Psychiatry

Background:

• Sequential treatment decisions in depression lack clear optimization strategies after initial treatment failure.
• Current health economic models for depression often use state-transition models, which may lack flexibility.

Purpose of the Study:

• To explore the suitability of Markov decision processes (MDPs) for optimizing sequential treatment decisions in depression.
• To formally compare MDPs with traditional state-transition models in health economic decision analysis.

Main Methods:

• Conducted two systematic reviews: one on MDP applications in healthcare and another on health economic models for depression.
• Reformulated an existing health economic model for depression using an MDP approach to illustrate its application and compare model structures.

Main Results:

• MDPs offer greater flexibility in modeling actions and rewards compared to state-transition models.
• Existing health economic models for depression predominantly use state-transition structures.
• The MDP reformulation allowed for dynamic comparisons of more interventions over time than traditional models.

Conclusions:

• Markov decision processes are a powerful tool for optimizing sequential treatment decisions in depression, offering advantages in dynamic treatment comparisons.
• Existing state-transition models are often too basic for evaluating sequential depression treatments, but MDP assumptions can be met with elaborate models.