When a decision depends on disease progression over time, a Markov model can provide a robust, readable structure for economic evaluation.
Representing patient progression
A Markov model describes how a population moves between health states over time: stable disease, progression, complication, treatment response, relapse, or death depending on the context.
This structure is particularly useful for chronic diseases, cancers, preventive interventions, or situations where costs and benefits accumulate over several years.
Connecting transitions, costs, and outcomes
Each health state can be associated with costs, utilities, event risks, and transition probabilities. The model can then estimate cumulative costs, life years, QALYs, and cost-effectiveness ratios.
Its strength lies in making the assumptions behind the decision explicit.
Choosing the right structure
A Markov model is not always necessary. In some cases, a decision tree or simpler analysis is sufficient. In others, microsimulation or partitioned survival modeling may be more appropriate.
The choice depends on the disease, time horizon, required level of detail, and available data.
Health states should tell a clinical story
The quality of a Markov model depends heavily on the definition of health states. These states should not be chosen only to simplify calculation; they should represent understandable and clinically meaningful situations that differ in costs, risks, or quality of life.
A model becomes more credible when clinicians, economists, and decision-makers can recognize the real patient pathway in its structure.
Validation is as important as construction
Building a model is not enough. Its internal logic must be checked, results compared with external sources when possible, sensitive parameters tested, and methodological choices documented.
This validation phase protects against invisible errors and strengthens confidence in the results presented.
A powerful tool when it remains readable
Complexity should not prevent understanding. A good model should remain auditable, documented, and able to support discussion with non-specialist decision-makers.
Santicxis designs and adapts decision models that combine technical rigor, methodological transparency, and strategic usefulness.