OBJECTIVE: Markov models are the standard method used in cost-effectiveness studies to represent the natural history of disease. The objective of this study was to show the key elements in building probabilistic Markov models. METHODS: We used the example of a new treatment for a generic disease. A probabilistic Markov model was constructed using statistical distributions. Monte Carlo simulations were carried out to obtain the probabilistic sensitivity analysis. The results were analyzed in terms of the cost-effectiveness plane and acceptability curve. RESULTS: The incremental cost-effectiveness rate for the average patient was 22,855 euro/quality adjusted life years (QALY). In the probabilistic sensitivity analysis, the results from all simulations were located in the northeast quadrant, corresponding to positive cost and effectiveness. However, 67% of the simulations were below the threshold of 30,000 euro/QALY. CONCLUSION: The use of probabilistic Markov models requires the integration of concepts from economics, epidemiology, statistics, and the clinical setting. Some stages of the process, such as the construction and processing of these models, the management of absolute and relative risks and of statistical distributions, often pose major difficulties but are key steps required to reproduce the disease with validity. Copyright 2009 SESPAS. Published by Elsevier Espana. All rights reserved.
OBJECTIVE: Markov models are the standard method used in cost-effectiveness studies to represent the natural history of disease. The objective of this study was to show the key elements in building probabilistic Markov models. METHODS: We used the example of a new treatment for a generic disease. A probabilistic Markov model was constructed using statistical distributions. Monte Carlo simulations were carried out to obtain the probabilistic sensitivity analysis. The results were analyzed in terms of the cost-effectiveness plane and acceptability curve. RESULTS: The incremental cost-effectiveness rate for the average patient was 22,855 euro/quality adjusted life years (QALY). In the probabilistic sensitivity analysis, the results from all simulations were located in the northeast quadrant, corresponding to positive cost and effectiveness. However, 67% of the simulations were below the threshold of 30,000 euro/QALY. CONCLUSION: The use of probabilistic Markov models requires the integration of concepts from economics, epidemiology, statistics, and the clinical setting. Some stages of the process, such as the construction and processing of these models, the management of absolute and relative risks and of statistical distributions, often pose major difficulties but are key steps required to reproduce the disease with validity. Copyright 2009 SESPAS. Published by Elsevier Espana. All rights reserved.
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