PharmacoEconomics

, Volume 24, Issue 11, pp 1043–1053 | Cite as

Recent Developments in Decision-Analytic Modelling for Economic Evaluation

  • Milton C. Weinstein
Conference Paper

Abstract

The past few years have seen rapid changes in the methods of decision-analytic modelling of healthcare programmes for the purposes of economic evaluation. This paper focuses on four developments in modelling that have emerged over the past few years or have become more widely used.

First, no one optimal method for extrapolating outcomes from clinical trials has yet been established. Modellers may draw from a set of varied assumptions about survival extrapolation that encompass a range of possibilities from highly optimistic to extremely cautious.

Secondly, the practicality and appeal of microsimulation as a method for analysing healthcare decision problems has increased dramatically with the speed of computing technology. Individual instantiations of a system are generated by using a random process to draw from probability distributions a large number of times (also known as Monte Carlo or probabilistic simulation). Microsimulation is moving in new directions, such as discrete-event simulations that simulate sequences of events by drawing directly from probability distributions of event times; this approach is now being broadly applied to model situations where populations of patients interact with healthcare delivery systems. Microsimulation modelling of transmission systems at the population level is also rapidly developing.

Thirdly, model calibration is emerging as a new tool that may offer health scientists a means of generating important fundamental knowledge about disease processes. Model calibration allows evidence synthesis in which observations on observable quantities are used to draw inferences about unobservable quantities. The methodology of model calibration has advanced considerably, drawing on theories of numerical analysis and mathematical programming such as gradient methods, intelligent grid search algorithms, and many more.

As a fourth issue, an area of extraordinary activity is in the use of transmission models to analyse interventions for infectious diseases, including population-wide effects of vaccination. Transmission models use differential equations to simulate, deterministically for the most part, transitions among infection-related health states. Only recently have modelling methodologies been combined so that cost-effectiveness analyses can consider explicitly not only the patient-level benefits of interventions but also the secondary benefits through transmission dynamics.

Advances in technology allow more realistic and complex healthcare models to be simulated more rapidly. However, decision makers will not readily accept results from models unless they can understand them intuitively and explain them to others in relatively simple terms. The challenge for the next generation of modellers is not only to harness the power available from these newly accessible methods, but also to extract from the new generation of models the insights that will have the power to influence decision makers.

References

  1. 1.
    Weinstein MC, Stason WB. Foundations of cost-effectiveness analysis for health and medical practices. N Engl J Med 1977; 296: 716–721PubMedCrossRefGoogle Scholar
  2. 2.
    Weinstein MC, Toy EL, Sandberg EA, et al. Modeling for health care and other policy decisions: uses, roles, and validity. Value Health 2001; 4: 348–361PubMedCrossRefGoogle Scholar
  3. 3.
    Weinstein MC, O’Brien B, Hornberger J, et al. Principles of good research 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
  4. 4.
    Claxton KP, Sculpher MJ. Using value of information analysis to prioritise health research. Pharmacoeconomics 2006; 24 (11): 1055–1068PubMedCrossRefGoogle Scholar
  5. 5.
    Weinstein MC, Stason WB. Cost-effectiveness of coronary artery bypass surgery. Circulation 1982; 66 (5 Pt 2): III56–III66PubMedGoogle Scholar
  6. 6.
    Kuntz K, Weinstein M. Modelling in economic evaluation. In: Drummond M, McGuire A, editors. Economic evaluation in health care: merging theory with practice. Oxford: Oxford University Press, 2001Google Scholar
  7. 7.
    Chilcott J, McCabe C, Tappenden P, et al. Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis. BMJ 2003; 326: 522–526PubMedCrossRefGoogle Scholar
  8. 8.
    Kobelt G, Jonsson L, Fredrikson S. Cost-utility of interferon beta-1b in the treatment of patients with active relapsing-remitting or secondary progressive multiple sclerosis. Eur J Health Econ 2003; 4: 50–59PubMedCrossRefGoogle Scholar
  9. 9.
    Prosser LA, Kuntz KM, Bar-Or A, et al. Cost-effectiveness of interferon beta-1a, interferon beta-1b, and glatiramer acetate in newly diagnosed non-primary progressive multiple sclerosis. Value Health 2004; 7: 554–568PubMedCrossRefGoogle Scholar
  10. 10.
    Buxton M. Economic evaluation and decision making in the UK. Pharmacoeconomics 2006; 24 (11): 1133–1143PubMedCrossRefGoogle Scholar
  11. 11.
    Hunink M, Glasziou P, Siegel J, et al. Decision making in health and medicine: integrating evidence and values. Cambridge: Cambridge University Press, 2001: Chap. 11Google Scholar
  12. 12.
    Griffin S, Claxton K, Hawkins N, et al. Probabilistic analysis and computationally expensive models: necessary and required? Value Health 2006; 9: 244–252PubMedCrossRefGoogle Scholar
  13. 13.
    Weinstein MC, Goldie SJ, Losina E, et al. Genotypic resistance testing to guide the choice of therapy in HIV: clinical impact and cost-effectiveness. Ann Intern Med 2001; 134: 440–450PubMedGoogle Scholar
  14. 14.
    Freedberg KA, Losina E, Weinstein MC, et al. The cost-effectiveness of combination antiretroviral therapy in HIV. N Engl J Med 2001; 344: 824–831PubMedCrossRefGoogle Scholar
  15. 15.
    Paltiel AD, Weinstein MC, Kimmel AD, et al. Expanded screening for HIV in the United States: an analysis of cost-effectiveness. New Engl J Med 2005; 352: 586–595PubMedCrossRefGoogle Scholar
  16. 16.
    Sax PE, Islam R, Walensky RP, et al. Should resistance testing be performed in treatment-naive HIV-infected patients? A cost-effectiveness analysis. Clin Infect Dis 2005; 41: 1316–1323PubMedCrossRefGoogle Scholar
  17. 17.
    Ades AE, Cliffe S. Markov chain Monte Carlo estimation of a multiparameter decision model: consistency of evidence and the accurate assessment of uncertainty. Med Decis Making 2002; 22: 359–371PubMedGoogle Scholar
  18. 18.
    Weinstein MC, Coxson PG, Williams LW, et al. Forecasting coronary heart disease incidence, mortality, and cost: the Coronary Heart Disease Policy Model. Am J Public Health 1987; 77: 1417–1426PubMedCrossRefGoogle Scholar
  19. 19.
    Hunink MGM, Goldman L, Tosteson ANA, et al. The recent decline in mortality from coronary heart disease, 1980–1990: the effect of secular trends in risk factors and treatment. JAMA 1997; 277: 535–542PubMedCrossRefGoogle Scholar
  20. 20.
    Salomon JA, Weinstein MC, Hammitt JK, et al. Empirically calibrated model of hepatitis C virus infection in the United States. Am J Epidemiol 2002; 156: 761–773PubMedCrossRefGoogle Scholar
  21. 21.
    Fryback DG, Stout NK, Rosenberg MA, et al. Chapter 7: the Wisconsin Breast Cancer Epidemiology Simulation Model. J Natl Cancer Inst Monogr 2006; (36): 37–47PubMedCrossRefGoogle Scholar
  22. 22.
    Anderson RM, May RM. Infectious diseases of humans: dynamics and control. Oxford: Oxford University Press, 1992Google Scholar
  23. 23.
    Axnick NW, Shavell SM, Witte JJ. Benefits due to immunization against measles. Public Health Reports 1969; 84: 673–680PubMedCrossRefGoogle Scholar
  24. 24.
    Koplan JP, Schoenbaum SC, Weinstein MC, et al. Pertussis vaccine: an analysis of benefits, risks, and costs. N Engl J Med 1979; 301: 906–911PubMedCrossRefGoogle Scholar
  25. 25.
    Sanders GD, Bayoumi AM, Sundaram V, et al. Cost-effectiveness of screening for HIV in the era of highly active antiretroviral therapy. N Engl J Med 2005; 352: 570–585PubMedCrossRefGoogle Scholar
  26. 26.
    Edmunds WJ, Medley GF, Nokes DJ. Evaluating the cost-effectiveness of vaccination programmes: a dynamic perspective. Stat Med 1999; 18: 3263–3282PubMedCrossRefGoogle Scholar
  27. 27.
    Resch SC, Salomon JA, Murray M, et al. Cost-effectiveness of treating multidrug-resistant tuberculosis. PLoS Med 2006 Jul 4; 3 (7): e241PubMedCrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2006

Authors and Affiliations

  • Milton C. Weinstein
    • 1
  1. 1.Department of Health Policy and ManagementHarvard School of Public HealthBostonUSA

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