, Volume 26, Issue 2, pp 131–148

Modelling Methods for Pharmacoeconomics and Health Technology Assessment

An Overview and Guide
Practical Application


This paper provides an overview of, and guidance as to when, why and how to choose and use, different simulation modelling methods as applied to healthcare. What simulation is and why it is necessary in addressing healthcare problems are discussed. In addition, key criteria for choosing an appropriate method (project type, population resolution, interactivity, treatment of time and space, resource constraints, autonomy and how knowledge is embedded) are covered. Key concepts for each method, moving from the simplest to most complex methods, are reviewed in some detail.


  1. 1.
    Freund D, Dittus R. Principles of pharmac oec onomic analysis of drug therapy. Pharmacoeconomics 1992; 1: 20–31PubMedCrossRefGoogle Scholar
  2. 2.
    Spaulding C, Daemen J, Boersma E, et al. A pooled analysis of data comparing sirolimus-eluting stents with bare-metal stents. N Engl J Med 2007; 356: 989–997PubMedCrossRefGoogle Scholar
  3. 3.
    Fenton J, Taplin S, Carney P, et al. Influence of computer-aided detection on performance of screening mammography. N Engl J Med 2007; 356: 1399–1409PubMedCrossRefGoogle Scholar
  4. 4.
    Boden W, O’Rourke R, Teo K, et al. Optimal medical therapy with or without PCI for stable coronary disease. N Engl J Med 2007; 15: 1503–1516CrossRefGoogle Scholar
  5. 5.
    Varmus H, Satcher D. Ethical complexities of conducting research in developing countries. N Engl J Med 1997; 337: 1003–1005PubMedCrossRefGoogle Scholar
  6. 6.
    Hegselmann R, Mueller U, Troitzsch K. Modelling and simulation in the social sciences from the philosophy of science point of view. Dordrecht: Kluwer Academic Publishers, 2005Google Scholar
  7. 7.
    Morgan M, Morrison M. Models as mediators: perspectives on natural and social science. New York: Cambridge University Press, 1999CrossRefGoogle Scholar
  8. 8.
    Kelton W. Experimental design for simulation. Proceedings of the 2000 Winter Simulation Conference; 2000 Dec 10–13; Orlando (FL): 32–38Google Scholar
  9. 9.
    Gold M, Siegel J, Russell L, Weinstein M. Cost-effectiveness in health and medicine. 1st ed. New York: Oxford University Press, 1996Google Scholar
  10. 10.
    Law A, Kelton W. Simulation modelling and analysis. New York: McGraw-Hill, 1991Google Scholar
  11. 11.
    Glenney N, Mac kulak G. Modelling and simulation provide key to CIM implementation philosophy. Ind Eng 1985; 17: 76–94Google Scholar
  12. 12.
    Stahl J, Rattner D, Wiklund R, et al. Reorganizing the system of care surrounding laparoscopic surgery: a cost-effectiveness analysis using discrete-event simulation. Med Dec Making 2004; 24: 461–471CrossRefGoogle Scholar
  13. 13.
    Stahl J, Sandberg W, Daily B, et al. Reorganizing patient care and workflow in the operating room: a cost-effectiveness study. Surgery 2006; 139 (6): 717–728PubMedCrossRefGoogle Scholar
  14. 14.
    Bateman R, Bowden R, Gogg T, et al. System improvement using simulation. Orem (UT): ProModel Corp, 1997Google Scholar
  15. 15.
    Shingo S. The Shingo production management system: improving process functions. Cambridge (MA): Productivity Press, 1992Google Scholar
  16. 16.
    Law A, McComas M. How simulation pays off. J Manufact Engineer 1988; 100: 37–39Google Scholar
  17. 17.
    Goeller B, Abraham S, Abrahamse A, et al. Policy analysis of water management for the Netherlands: vol. 1. Summary report. Santa Monica (CA): Rand Corporation, 1983Google Scholar
  18. 18.
    Hancock W, Dissen R, Merten A. An example of simulation to improve plant productivity. AIIE Transactions 1977: 2–10Google Scholar
  19. 19.
    Freund D, Dittus R. Principles of pharmacoec onomic analysis of drug therapy. Pharmacoeconomics 1992; 1: 20–31PubMedCrossRefGoogle Scholar
  20. 20.
    Mitchell M, Jolley J. Research design explained. New York: Harcourt, 2001Google Scholar
  21. 21.
    Reis H, Judd C. Handbook of research methods in social and personality psychology. Cambridge (UK): Cambridge University Press, 2000Google Scholar
  22. 22.
    Shadish W, Cook T, Campbell D. Experimental and quasi-experimental designs for generalized causal inference. Boston (MA): Houghton Mifflin, 2002Google Scholar
  23. 23.
    Nuijten M. The selection of data sources for use in modelling studies. Pharmacoeconomics 1998; 13: 305–316PubMedCrossRefGoogle Scholar
  24. 24.
    Nuijten M. Incorporation of statistical uncertainty in health economic modelling studies using second-order Monte Carlo simulations. Pharmacoeconomics 2004; 22: 759–769PubMedCrossRefGoogle Scholar
  25. 25.
    Briggs A. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics 2000; 17: 479–500PubMedCrossRefGoogle Scholar
  26. 26.
    Halpem E, Weinstein M, Hunink M, et al. Representing both first- and second-order uncertainties by Monte Carlo simulation for groups of patients. Med Dec is Making 2000; 20 (3): 314–322CrossRefGoogle Scholar
  27. 27.
    Doubilet P, Begg C, Weinstein M, et al. Probabalistic sensitivity analysis using Monte Carlo simulation: a practical approach. Med Decis Making, 1985; 5: 157–177PubMedCrossRefGoogle Scholar
  28. 28.
    Barton P, Bryan S, Robinson S. Modelling in the economic evaluation of health care: selecting the appropriate approach. J Health Serv Res Policy 2004; 9: 110–118PubMedCrossRefGoogle Scholar
  29. 29.
    Brennan A, Chick S, Davies R. A taxonomy of model structures for economic evaluation of health technologies. Health Econ 2006; 15: 1295–1310PubMedCrossRefGoogle Scholar
  30. 30.
    Plante D, Kassirer J, Zarin D, et al. Clinical decision consultation service. Am J Med 1986; 80: 1169–1176PubMedCrossRefGoogle Scholar
  31. 31.
    Barnabas R, Laukkanen P, Koskela P, et al. Epidemiology of HPV 16 and cervical cancer in Finland and the potential impact of vaccination: mathematical modelling analyses. PLoS Med 2006; 3: 624–632CrossRefGoogle Scholar
  32. 32.
    Eckman M, Levine H, Salem D, et al. Making decisions about antithrombotic therapy in heart disease: decision analytic and cost-effectiveness issues. Chest 1998; 114: 699S–714SPubMedCrossRefGoogle Scholar
  33. 33.
    Eddy D. The frequency of cervical cancer screening: comparison of a mathematical model with empirical data. Cancer 1987; 60: 1117–1122PubMedCrossRefGoogle Scholar
  34. 34.
    US Congress, Office of Technology Assessment. Rural emergency medical services: special report, OTA-H-445. Washington, DC: US Government Printing Office, 1989 NovGoogle Scholar
  35. 35.
    Pritsker A, Martin D, Reust J. Organ transplantation policy evaluation. Proceedings of the Winter Simulation Conference; 1995 Dec 3–6; Arlington (VA): 1314–1323Google Scholar
  36. 36.
    Stahl J, Roberts M, Gazelle G. Optimizing the management and financial performance of the teaching ambulatory care clinic. J Gen Intern Med 2003; 18: 1–9CrossRefGoogle Scholar
  37. 37.
    Zenios S. Modelling the transplant waiting list: a queuing model with reneging. Queuing Systems 1999; 31: 239–251CrossRefGoogle Scholar
  38. 38.
    Quaranta V, Weaver A, Cummings P, et al. Mathematical modelling of cancer: the future of prognosis and treatment. Clin Chim Acta 2005; 357: 173–179PubMedCrossRefGoogle Scholar
  39. 39.
    Dean B, Gallivan S, Barber N, et al. Mathematical modelling of pharmacy systems. Am J Health Syst Pharm 1997; 54: 2491–2499PubMedGoogle Scholar
  40. 40.
    Verma B, Ray S, Srivastava R. Mathematical models and their applications in medicine and health. Health Popul Perspect Issues 1981; 4: 42–58PubMedGoogle Scholar
  41. 41.
    England W, Roberts S. Immunization to prevent insulin-dependent diabetes mellitus? The economics of genetic screening and vaccination for diabetes. Ann Intern Med 1981; 94: 395–400PubMedGoogle Scholar
  42. 42.
    Fineberg H. Decision trees: construction, uses, and limits. Bull Cancer 1980; 67: 395–404PubMedGoogle Scholar
  43. 43.
    Grinstead C, Snell J. Introduction to probability. Providence (RI): American Mathematical Society, 1997Google Scholar
  44. 44.
    Sonnenberg F, Beck J. Markov models in medical decision making: a practical guide. Med Dec Mak 1993; 13: 322–338CrossRefGoogle Scholar
  45. 45.
    Eckman M, Rosand J, Knudsen K, et al. Can patients be anticoagulated after intracerebral hemorrhage? A decision analysis. Stroke 2003; 34: 1710–1716PubMedCrossRefGoogle Scholar
  46. 46.
    Beck J, Pauker S. The Markov process in medical decision making. Med Decis Making 1983; 3: 419–458PubMedCrossRefGoogle Scholar
  47. 47.
    TreeAge Software, Inc. [online]. Available from URL: [Accessed 2007 Nov 27]
  48. 48.
    Lumina Decision Systems, Inc. [online]. Available from URL: [Accessed 2007 Nov 27]Google Scholar
  49. 49.
    Metropolis N, Ulam S. The Monte Carlo method. J Am Stat Assoc 1949 44: 335–341PubMedCrossRefGoogle Scholar
  50. 50.
    Bellman R. Dynamic programming. Mineola (NY): Dover Publications, 2003Google Scholar
  51. 51.
    Marbach P, Tsitsiklis J. Simulation-based optimization of Markov reward processes (technical report LIDS-P 2411) [online]. Available from URL: 592383.html [Accessed 2007 Nov 27]Google Scholar
  52. 52.
    Puterman M. Markov decision processes: discrete stochastic dynamic programming. Hoboken (NJ): John Wiley & Sons, Inc., 2005Google Scholar
  53. 53.
    Feinberg EA, editor. Handbook of Markov decision processes: methods and applications. Boston (MA): Kluwer Academic Publishers, 2005Google Scholar
  54. 54.
    Schaefer A, Bailey M, Shechter S, Roberts M. Modelling medical treatment using Markov decision processes. In: Brandeau ML, Sainfort F, Pierskalla WP, editors. Operations research and health care: a handbook of methods and applications. Boston (MA): Kluwer, 2004: 593–612Google Scholar
  55. 55.
    MathWorks, Inc. [online]. Available from URL: [Accessed 2007 Nov 27]
  56. 56.
    Pearl J. Causal inference in the health sciences: a conceptual introduction. Health Serv Outcomes Res Methodol 2001; 2: 189–220CrossRefGoogle Scholar
  57. 57.
    Nease R, Owens D. Use of influence diagrams to structure medical decisions. Med Decis Making 1997; 17: 263–275PubMedCrossRefGoogle Scholar
  58. 58.
    Owens D, Shachter R, Nease R. Representation and analysis of medical decision problems with influence diagrams. Med Decis Making 1997; 17: 241–262PubMedCrossRefGoogle Scholar
  59. 59.
    Sonnenberg A, Collins J. Vicious circles in inflammatory bowel disease. Inflamm Bowel Dis 2006; 12: 944–949PubMedCrossRefGoogle Scholar
  60. 60.
    Meyer J, Phillips M, Cho P, et al. Application of influence diagrams to prostate intensity-modulated radiation therapy plan selection. Phys Med Biol 2004; 49: 1637–1653PubMedCrossRefGoogle Scholar
  61. 61.
    MacDiarmid S, Pharo H. Risk analysis: assessment, management and communication. Rev Sci Tech 2003; 22: 397–408PubMedGoogle Scholar
  62. 62.
    Coyle R. A systems approach to the management of a hospital for short-term patients. Socioecon Plann Sci 1984; 18: 219–226PubMedCrossRefGoogle Scholar
  63. 63.
    Norsys Software Corp. [online]. Available from URL: [Accessed 2007 Nov 27]
  64. 64.
    Forrester J. Industrial dynamics. Cambridge (MA): MIT Press, 1961Google Scholar
  65. 65.
    Taylor K, Lane D. Simulation applied to health services: opportunities for applying the system dynamics approach. J Health Serv Res Policy 1998; 3: 226–232PubMedGoogle Scholar
  66. 66.
    Hoard M, Homer J, Manley W, et al. Systems modelling in support of evidence-based disaster planning for rural areas. Int J Hyg Environ Health 2005; 208: 117–125PubMedCrossRefGoogle Scholar
  67. 67.
    Edmunds W, Medley G, Nokes D. Evaluating the cost-effectiveness of vaccination programmes: a dynamic approach. Stat Med 1999; 18: 3263–3282PubMedCrossRefGoogle Scholar
  68. 68.
    System Dynamics Society [online]. Available from URL: [Accessed 2007 Nov 27]
  69. 69.
    Banks J, editor. Handbook of simulation. New York: John Wiley & sons, Inc., 1998Google Scholar
  70. 70.
    Institute for Operations Research and the Management Sciences. Informs online [online]. Available from URL: [Accessed 2007 Dec 19]
  71. 71.
    Minar N, Burkhart R, Langton C, et al. The Swarm simulation system: a toolkit for building multi-agent simulations [working paper 96-06-042]. Santa Fe (NM): Santa Fe Institute, 1996Google Scholar
  72. 72.
    Tesfatsion L, Judd K, editors. Handbook of computational economics: 2. Agent-based computational economics. Amsterdam: North-Holland, Elsevier, 2006Google Scholar
  73. 73.
    Swarm Development Group [online]. Available from URL: [Accessed Dec 19]
  74. 74.
    University of Michigan, Center for the Study of Complex Systems. RePast at CSCS [online]. Available from URL: [Accessed Dec 19]
  75. 75.
    Christley R, Pinchbeck G, Bowers R, et al. Infection in social networks: using network analysis to identify high-risk individuals. Am J Epidemiol 2005; 162: 1024–1031PubMedCrossRefGoogle Scholar
  76. 76.
    Christakis N. Social network analysis and collateral health effects. BMJ 2004; 329: 184–185PubMedCrossRefGoogle Scholar
  77. 77.
    Christakis N, Fowler J. The spread of obesity in a large social network over 32 years. N Engl J Med 2007; 357: 370–379PubMedCrossRefGoogle Scholar
  78. 78.
    Analytic Technologies, Inc. [online]. Available from URL: [Accessed Dec 19]

Copyright information

© Adis Data Information BV 2008

Authors and Affiliations

  1. 1.MGH — Institute for Technology Assessment, Massachusetts General HospitalBostonUSA

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