Abstract
Background
Various software packages are commonly used for the implementation and calculation of decision-analytic models for health economic evaluations. However, comparison of these programs with regard to ease of implementing a model is lacking.
Objectives
(i) to compare the assets and drawbacks of three commonly used software packages for Markov models with regard to ease of implementation; and (ii) to investigate how a technical model validation can be conducted by comparing the results of the three implementations.
Methods
A Markov model on chronic obstructive pulmonary disease was implemented in TreeAge, Microsoft® Excel and Arena® with the same assumptions on model structure, transition probabilities and costs. A hypothetical smoking cessation programme for patients in stage 1 was evaluated against usual care. The packages were compared with respect to time and effort for implementation, run-time, features for the presentation of results, and flexibility. Agreement between the packages on average costs and life-years gained and on the incremental cost-effectiveness ratio was considered for technical validation in the form of expected values (between TreeAge and Excel only) and Monte Carlo simulations.
Results
Ease of implementation was best in TreeAge, whereas Arena® offered the highest flexibility. Deterministic results were in agreement between TreeAge and Excel, as were simulated values between all three packages.
Conclusions
Excel offers an intuitive spreadsheet interface, but the acquisition of and the training in TreeAge or Arena® is worthwhile for more complex models. Double implementation is a practicable validation technique that should be conducted to ensure correct model implementation.
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References
Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press, 2005
Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998; 13(4): 397–409
Brennan A, Chick SE, Davies R. A taxonomy of model structures for economic evaluation of health technologies. Health Econ 2006 Dec; 15: 1295–310
Stahl JE. Modelling methods for pharmacoeconomics and health technology assessment: an overview and guide. Pharmacoeconomics 2008; 26 (2): 131–48
Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. In: Gray A, Briggs A, editors. Oxford: Oxford University Press, 2006
Hammerschmidt T, Goertz A, Wagenpfeil S, et al. Validation of health economic models: the example of EVITA. Value Health 2003 Sep–Oct; 6: 551–9
Krahn MD, Naglie G, Naimark D, et al. Primer on medical decision analysis: part 4. Analyzing the model and interpreting the results. Med Decis Making 1997 Apr–Jun; 17: 142–51
Philips Z, Bojke L, Sculpher M, et al. Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics 2006; 24 (4): 355–71
Sculpher M, Fenwick E, Claxton K. Assessing quality in decision analytic cost-effectiveness models: a suggested framework and example of application. Pharmacoeconomics 2000; 17 (5): 461–77
Simpson KN, Strassburger A, Jones WJ, et al. Comparison of Markov model and discrete-event simulation techniques for HIV. Pharmacoeconomics 2009; 27 (2): 159–65
Karnon J. Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation. Health Econ 2003 Oct; 12: 837–48
Briggs AH. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics 2000; 17 (5): 479–500
Caro JJ. Pharmacoeconomic analyses using discrete event simulation. Pharmacoeconomics 2005; 23 (4): 323–32
Le Lay A, Despiegel N, Francois C, et al. Can discrete event simulation be of use in modelling major depression? Cost Eff Resour Alloc 2006 Dec 5; 4: 19 [online]. Available from URL: http://www.resource-allocation.com/content/4/1/19 [Accessed 2009 Jul 29]
Sendi PP, Craig BA, Pfluger D, et al., on behalf of the Swiss HIV Cohort Study. Systematic validation of disease models for pharmacoeconomic evaluations. J Eval Clin Pract 1999 Aug; 5: 283–95
GOLD: Global Initiative for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. Bethesda (MD): National Institutes of Health, National Heart, Lung and Blood Institute, 2006
Spencer M, Briggs AH, Grossman RF, et al. Development of an economic model to assess the cost effectiveness of treatment interventions for chronic obstructive pulmonary disease. Pharmacoeconomics 2005; 23 (6): 619–37
Kelton WD, Sadowski RP, Sturrock DT. Simulation with Arena. Boston (MA): McGraw Hill Higher Education, 2003
Rockwell Automation. Arena basic edition [online]. Available from URL: http://www.arenasimulation.com/products/basic_edition.asp [Accessed 2009 Jul 29]
TreeAge software [online]. Available from URL: http://server.treeage.com/treeagepro/purchase/index.asp [Accessed 2009 Jul 29]
Microsoft Office Excel 2007 [online]. Available from URL: http://store.microsoft.com/microsoft/Office-Excel-2007/product/C8CAFA6A [Accessed 2009 Jul 29]
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Menn, P., Holle, R. Comparing Three Software Tools for Implementing Markov Models for Health Economic Evaluations. Pharmacoeconomics 27, 745–753 (2009). https://doi.org/10.2165/11313760-000000000-00000
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DOI: https://doi.org/10.2165/11313760-000000000-00000