PharmacoEconomics

, Volume 23, Issue 4, pp 323–332

Pharmacoeconomic analyses using discrete event simulation

Current Opinion

Abstract

To date, decision trees and Markov models have been the most common methods used in pharmacoeconomic evaluations. Both of these techniques lack the flexibility required to appropriately represent clinical reality. In this paper an alternative, more natural, way to model clinical reality — discrete event simulation — is presented and its application is illustrated with a real world example.

A discrete event simulation represents the course of disease very naturally, with few restrictions. Neither mutually exclusive branches nor states are required, nor is a fixed cycle. All relevant aspects can be incorporated explicitly and efficiently. Flexibility in handling perspectives and carrying out sensitivity analyses, including structural variations, is incorporated and the entire model can be presented very transparently. The main limitations are imposed by lack of data to fit realistic models.

Discrete event simulation, though rarely employed in pharmacoeconomics today, should be strongly considered when carrying out economic evaluations, particularly those aimed at informing policy makers and at estimating the budget impact of a pharmaceutical intervention.

References

  1. 1.
    Weinstein MC, O’Brien B, Homberger J, et al. Principles of good practice for decision analytic modeling in health care evaluation: report of the ISPOR Task Force on good research practices: modeling studies. Value Health 2003; 6: 9–17PubMedCrossRefGoogle Scholar
  2. 2.
    Academy of Managed Care Pharmacy. Format for formulary submissions: version 2.0. AMCP, 2002 [online]. Available from URL: http://www.amep.org/data/navcontent/formatv20%2Epdf [Accessed 2002 Oct]
  3. 3.
    Greenberg PE, Arcelus A, Birnbaum HG, et al. Pharmacoeconomics and health policy. Current applications and prospects for the future. Pharmacoeconomics 1999; 16: 425–32PubMedCrossRefGoogle Scholar
  4. 4.
    Chang K Nash D. The role of pharmacoeconomic evaluations in disease management. Pharmacoeconomics 1998; 14: 11–7PubMedCrossRefGoogle Scholar
  5. 5.
    Weinstein MC, Toy EL, Sandberg EA, et al. Modeling for health care and other policy decisions: uses, roles and validity. Value Health 2001; 4: 348–61PubMedCrossRefGoogle Scholar
  6. 6.
    Baltussen R, Leidl R, Ament A. Real world designs in economic evaluation: bridging the gap between clinical research and policy-making. Pharmacoeconomics 1999; 16: 449–58PubMedCrossRefGoogle Scholar
  7. 7.
    Caro JJ. Disease simulation models and health care decisions. CMAJ 2000; 162: 1001–2PubMedGoogle Scholar
  8. 8.
    Akehurst R, Anderson P, Brazier J, et al. Decision analytic modeling in the economic evaluation of health technologies. Pharmacoeconomics 2000; 17: 443–4CrossRefGoogle Scholar
  9. 9.
    Sonnenberg FA, Beck JR. Markov models in medical decision making. Med Decis Making 1993; 13: 322–38PubMedCrossRefGoogle Scholar
  10. 10.
    Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 1998; 13: 397–409PubMedCrossRefGoogle Scholar
  11. 11.
    Beck JR, Pauker SG. The Markov process in medical prognosis. Med Decis Making 1983; 3: 419–58PubMedCrossRefGoogle Scholar
  12. 12.
    Eisenberg JM. Why a journal of pharmacoeconomics? Pharmacoeconomics 1992; 1: 2–4PubMedCrossRefGoogle Scholar
  13. 13.
    Freund DA, Dittus RS. Principles of pharmacoeconomic analysis of drug therapy. Pharmacoeconomics 1992; 1: 20–31PubMedCrossRefGoogle Scholar
  14. 14.
    Strakowski SM, DelBello MP, Adler CM. Comparative efficacy and tolerability of drug treatments for bipolar disorder. CNS Drugs 2001; 15: 701–18PubMedCrossRefGoogle Scholar
  15. 15.
    Young RC, Biggs IT, Ziegler VE, et al. A rating scale for mania: reliability, validity, and sensitivity. Br J Psychiatry 1978; 133: 429–35PubMedCrossRefGoogle Scholar
  16. 16.
    Banks J, Carson IS, Nelson BL. discrete event system simulation. Englewood Cliffs: Prentice-Hall, 1996Google Scholar
  17. 17.
    Jonasson O. Waiting in line: should selected patients ever be moved up? Transpl Proc 1989; 21: 3390–4Google Scholar
  18. 18.
    Law AM, Kelton WD. Simulation modeling and analysis. Boston (MA): McGraw-Hill, 2000Google Scholar
  19. 19.
    Caro JJ, Huybrechts KIT, Klittich WS, et al. for the CORE Study Group. Allocating funds to prevention of cardiovascular disease in light of the NCEP ATPIII guidelines. Am J Managed Care 2003; 9: 477–89Google Scholar
  20. 20.
    Caro JJ, O JA, Klittich WS, et al. The economic impact of warfarin prophylaxis in non-valvular atrial fibrillation. Dis Manag Clin Outcomes 1997; 1: 1–7Google Scholar
  21. 21.
    Caro JJ, Salas M, O’Brien JA, et al. Modeling the efficiency of reaching a target intermediate endpoint: a case study in type 2 diabetes in the US. Value Health 2004; 7: 13–21PubMedCrossRefGoogle Scholar
  22. 22.
    Caro JJ, Huybrechts K. Stroke Treatment Economic Model (STEM): predicting long-term costs from functional status. Stroke 1999; 30: 2574–9PubMedCrossRefGoogle Scholar
  23. 23.
    Caro JJ, Ward A, O’Brien J. Lifetime costs of complications resulting from type 2 diabetes in the US. Diabetes Care 2002; 25: 476–81PubMedCrossRefGoogle Scholar
  24. 24.
    Caro JJ, O’Brien JA, Migliaccio-Walle K, et al. Economic analysis of initial HIV treatment: efavirenz versus indinavir. Pharmacoeconomics 2001; 19: 95–104PubMedCrossRefGoogle Scholar
  25. 25.
    Pegden CD, Shannon RE, Sadowski RP. Introduction to simulation using siman. Boston (MA): McGraw-Hill, 1990Google Scholar
  26. 26.
    Davies HTO, Davies R. Simulating health systems: modeling problems and software solutions. Fur J Oper Res 1995; 87: 3544Google Scholar
  27. 27.
    Bowden RO. The spectrum of simulation software. HE Solutions 1998; 30: 44–6Google Scholar
  28. 28.
    Kelton WD, Sadowski RP, Sadowski DA. Simulation with ARENA. Boston (MA): McGraw-Hill, 1998Google Scholar
  29. 29.
    Law AM. Statistical analysis of the output data from terminating simulations. Naval Res Logist Quart 1980; 27: 131–43CrossRefGoogle Scholar
  30. 30.
    Alexopoulos C, Seila AF. Output data analysis. In: Banks J, editor. Handbook of simulation. New York: John Wiley, 1998Google Scholar
  31. 31.
    Pawlikowski K. Steady-state estimation of queuing processes: a survey of problems and solutions. Commun Assoc Comput Mach 1990; 22: 123–70Google Scholar
  32. 32.
    Siegel JE, Torrance GW, Russell LB, et al. Guidelines for pharmacoeconomic studies: recommendations from the panel on cost effectiveness in health and medicine: panel on cost effectiveness in health and medicine. Pharmacoeconomics 1997; 11: 159–68PubMedCrossRefGoogle Scholar
  33. 33.
    Hatoum HT, Kong SX. How much faith can we have in pharmacoeconomic analyses? Pharmacoeconomics 1994; 6: 584–6PubMedCrossRefGoogle Scholar
  34. 34.
    Sheldon TA. Problems of using modeling in the economic evaluation of health care. Health Econ 1996; 5: 1–11PubMedCrossRefGoogle Scholar
  35. 35.
    Weinstein MC, Stason WB. Foundations of cost-effectiveness analysis for health and medical practices. N Engl J Med 1977; 296: 716–21PubMedCrossRefGoogle Scholar
  36. 36.
    Glick H, Kinosian B, Shulman K. Decision analytic modeling: some uses in the evaluation of new pharmaceuticals. Drug Inf J 1994; 28: 691–707CrossRefGoogle Scholar
  37. 37.
    Hazen G. Stochastic trees: a new technique for temporal medical decision modeling. Med Decis Making 1992; 12: 163–78PubMedCrossRefGoogle Scholar
  38. 38.
    Schmidt JW, Taylor RE. Simulation and analysis of industrial systems. Homewood: Richard D Irwin, 1970Google Scholar
  39. 39.
    Jun JB, Jacobson SH, Swisher JR. Application of discrete event simulation in health care and clinics: a survey. J Oper Res Soc 1999; 50: 109–23Google Scholar
  40. 40.
    Agro KE, Bradley CA, Mittmann N, et al. Sensitivity analysis in health economic and pharmacoeconomic studies: an appraisal of the literature. Pharmacoeconomics 1997; 11: 75–88PubMedCrossRefGoogle Scholar
  41. 41.
    Fu MC. Optimization via simulation: a review. Ann Operations Res 1994; 53: 199–247CrossRefGoogle Scholar
  42. 42.
    Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. BMJ 1996; 313: 275–83PubMedCrossRefGoogle Scholar
  43. 43.
    Schruben LW. Simulation modeling with event graphs. Commun Assoc Comput Mach 1983; 26: 957–63Google Scholar
  44. 44.
    Law AM. Simulation model’s level of detail determines effectiveness. Ind Eng 1991; 23: 16–8Google Scholar
  45. 45.
    Kamon J. Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation. Health Econ 2003; 12 (10): 83748Google Scholar
  46. 46.
    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 (2): 110–8PubMedCrossRefGoogle Scholar
  47. 47.
    Kamon J, Brown J. Selecting a decision model for economic evaluation: a case study and review. Health Care Manag Sci 1998; 1: 133–40CrossRefGoogle Scholar
  48. 48.
    Avramidis AN, Wilson JR. Integrated variance reduction strategies for simulation. Oper Res 1996; 44: 327–46CrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  1. 1.Caro Research InstituteConcordUSA

Personalised recommendations