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State-Transition Modeling

Methodpeer-reviewed

A way to simulate how diseases progress and how treatments affect people over time, helping compare different healthcare options.

At a glance

Use when

Modeling chronic diseases with multiple stages; long-term projections are needed; individual patient data is limited

Avoid when

Disease progression is highly continuous and not stage-based; insufficient data on transition probabilities

Inputs

Health state transition probabilities, time horizon, cycle length, treatment effects, costs, utilities

Outputs

Projected clinical outcomes (e.g., survival, quality-adjusted life years), cost-effectiveness measures (e.g., ICER), event probabilities over time

How it works

A simulation method for modeling disease progression and treatment outcomes over time in economic evaluations, typically using Markov models or other discrete-state frameworks to represent transitions between health states.

HTA domains
Clinical Effectiveness, Costs & Economic Evaluation, Patient and Social Aspects
Assumptions
Disease progression can be represented by discrete health states; transitions depend only on current state (Markov property) or can be extended; transition probabilities are stable over time or follow defined patterns
Strengths
Allows long-term extrapolation beyond trial data; accommodates competing risks and recurrent events; flexible structure for modeling complex pathways
Limitations
Sensitivity to model structure and assumptions; potential oversimplification of continuous processes; requires careful validation and parameter estimation

Questions this answers

References & sources

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Beta record. Based on the original catalogue summary; primary-source enrichment pending.