OBJECTIVES: While Cost Utility Analysis (CUA) is an integral part of the standard WHO and other guidelines for implementing HTAs, this paper aims at advancing CUA methodology through explicit incorporation and variation of spatial and temporal dynamics of patient populations and their probabilistic responses to treatment interventions. We present a “systems dynamic (SD) model” for designing and conducting a “dynamic cost utility analysis” (dCUA)  of three cGVHD treatment interventions – extracorporeal photopheresis (ECP), rituximab (Rmb), and imatinib (Imt) -- at a national scale (Spain) over a 10-year simulation horizon. METHODS: The SD model postulates that a continuous annual flux of cGvHD patients, heterogeneously distributed after skin, mucous membrane, lung, liver and gastrointestinal tract transplants, temporally evolve in three primary states (complete response, partial response and no response) and numerous secondary & tertiary states (e.g. live or die, or maintain or change treatment if alive etc.) in response to the three treatments; and the transitions between these states over time are governed by the differential efficacy of treatments/interventions. We use knowledge management approach to specify the parameters governing efficacy of treatments and costs & utilities of patients in specific response states. Monte Carlo simulations were conducted to estimate the medians and confidence intervals around cost utilities. RESULTS: We demonstrate the ability of SD models to enable design and implementation of dCUA for “dynamic” and “heterogeneous” groups of patient populations. From the application standpoint, our findings suggest ECP has a lower cost per quality adjusted life year compared with Rmb and Imt, but this differs across specific transplant types of patients and the time elapsed since transplant. CONCLUSIONS: We demonstrate that SD models can be successfully deployed to conduct dCUA and set up as decision support systems for HTAs and adapted across spatial and temporal scales.