Decision analysis in clinical radiology by means of Markov modeling

Markov models (Multistate transition models) are mathematical tools to simu late a cohort of individuals followed over time to assess the prognosis res ulting from different strategies. They are applied on the assumption that p ersons are in one of a finite number of states of health (Markov states). E ach condition is given a transition probability as well as an incremental v alue. Probabilities may be chosen constant or varying over time due to pred efined rules. Time horizon is divided into equal increments (Markov cycles) . The model calculates quality-adjusted life expectancy employing real-life units and values and summing up the length of time spent in each health st ate adjusted for objective outcomes and subjective appraisal. This sort of modeling prognosis for a given patient is analogous to utility in common de cision trees. Markov models can be evaluated by matrix algebra, probabilist ic cohort simulation and Monte Carlo simulation. They have been applied to assess the relative benefits and risks of a limited number of diagnostic an d therapeutic procedures in radiology. More interventions should be submitt ed to Markov analyses in order to elucidate their cost-effectiveness.