[Decision analysis in radiology using Markov models].
Markov models (Multistate transition models) are mathematical tools to simulate a cohort of individuals followed over time to assess the prognosis resulting from different strategies. They are applied on the assumption that persons are in one of a finite number of states of health (Markov states). Each condition is given a transition probability as well as an incremental value. Probabilities may be chosen constant or varying over time due to predefined 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 state adjusted for objective outcomes and subjective appraisal. This sort of modeling prognosis for a given patient is analogous to utility in common decision trees. Markov models can be evaluated by matrix algebra, probabilistic cohort simulation and Monte Carlo simulation. They have been applied to assess the relative benefits and risks of a limited number of diagnostic and therapeutic procedures in radiology. More interventions should be submitted to Markov analyses in order to elucidate their cost-effectiveness.
Golder
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Abteilung Radiologie und Nuklearmedizin, Klinikum Benjamin Franklin, Freie Universität Berlin. golder@ukbf.fu-berlin.de
D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D003661:Decision Support Techniques; D003663:Decision Trees; D006305:Health Status Indicators; D006801:Humans; D008390:Markov Chains; D009010:Monte Carlo Method; D019057:Quality-Adjusted Life Years; D011871:Radiology / Q000191:economics / Q000706:statistics & numerical data*