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June - Simulation for Health Economics Analysis

In this workshop Ron will discuss the benefits of discrete-event simulation for Health Economic investigations that are conducive for decision-making by payers and providers, and talk through a "Real-World" application example.

Today more than ever governments and health providers are under extreme pressure to reduce costs while maintaining patient quality of life. Accuracy of information presented to them is critical for acceptance. This workshop will justify the use of discrete-event simulation by health economists as a means to provide accurate assessments of value for medical products or services.

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June - Simulation for Health Economics Analysis

  1. 1. SIMUL8 Corporation | | info@SIMUL8.com1 800 547 6024 | +44 141 552 6888
  2. 2. SIMUL8 Corporation | | info@SIMUL8.com1 800 547 6024 | +44 141 552 6888•••
  3. 3. © GHEP 2013Health Economic ModelingAdvantages of Discrete-Event SimulationReal World SIMUL8 ModelQuestionsReferences4
  4. 4. Health Economic Modeling The International Society for Pharmacoeconomicsand Outcomes Research (ISPOR) Task Force on GoodResearch Practices – Modeling Studies:"[...] an analytic methodology that accounts for events overtime and across populations, that is based on data drawnfrom primary and/or secondary sources, and whosepurpose is to estimate the effects of an intervention onvalued health consequences and costs.“1
  5. 5. Health Economic Modeling The aim of health economic modeling is to generate expected values for theclinical and economic effects of therapeutic alternatives
  6. 6. Health Economic Modeling There are two quite distinct aspects of model-basedeconomic evaluation1. First, it is necessary to produce the mean estimate ofcost-effectiveness (or other outcome measures) for agiven set of parameters (Type of Model)2. Second is the issue of exploring the effects of uncertaintyin the model inputs (Sensitivity Analysis)7© GHEP 2013
  7. 7. Health Economic Modeling Decision Trees8© GHEP 2013
  8. 8. Health Economic Modeling Cohort or Individual State-Transition Models2,3 Cohort models aggregate the individuals into a group which becomes the unit of analysis.Over time this group “breaks up” into pre-defined subgroups according to the events beingmodeled (A). Individual models consider the experience of each patient individually, even ifthey report results at the level of the entire population. Each individual has uniquecharacteristics, on the basis of which their individual course is modeled (B).Individual-Based State Transition Model1Cohort-Based State Transition Model19© GHEP 2013
  9. 9. Health Economic Modeling State Transition Models (No Interaction) Markov and Monte Carlo Simulation2,3Markov ModelAdapted from Hepatitis B Model1Monte Carlo SimulationAdapted from Hepatitis B Model110© GHEP 2013
  10. 10. Health Economic Modeling Event-Based Models (Interaction) Discrete-Event Simulation (DES) Discrete event simulation (DES) is a flexible modeling method characterized by theability to represent complex behavior within, and interactions betweenindividuals, populations, and their environment4 Applications in health care have increased over the last 40 years5 and includebiologic models6,7, process redesign and optimization8–10, geographic allocation ofresources11,12, trial design13,14, and policy evaluation15–17 In health economics, DES is a preferred choice as it favors greater flexibility indepicting the cost-effectiveness of prevention or therapeutic interventions forchronic disease1811© GHEP 2013
  11. 11. Health Economic Modeling Basic Patient/Disease Pathway – Chronic Venous Leg Ulcer© GHEP 2013 12
  12. 12. Health Economic Modeling13© GHEP 2013
  13. 13. © GHEP 2013Health Economic ModelingAdvantages of Discrete-Event SimulationReal-World SIMUL8 ModelQuestionsReferences14
  14. 14. Advantages of Discreet-Event Simulation (My DES Model) Our study models healing progression, decision for best clinical pathway (community,clinic, specialist etc.), time of reoccurrence for each patient/number of woundsseparately, which requires a large number of attributes and events that likely exceedsthe manageable size of a Markov model. Further, the time to event (e.g. 100% healing, 75% healing, pain resolution etc.)depends on the time the patient has spent in different clinical situations (the previousattribute). Such “memories” can be attached to the individuals in a DES model, which is difficult toachieve with a cohort Markov approach In a DES model, individual life histories are created by drawing randomly fromdistributions that describe the time to the occurrence of particular events. Theindividuals from the study population would move from one attribute to another, drivenby events, by means of time to progression of wound severity, time to decision forhospitalization, probability of infection and death, survival time of wounds to healing,and time to death.© GHEP 2013 15
  15. 15. Advantages of Discrete-Event Simulation18-25 Represents clinical reality Presents the course of disease naturally with few restrictions Is flexible: no mutually exclusive branches or states required Follows the natural concept of time, the simulation clock keeps track of the passage oftime (no fixed cycles) Offers flexibility handling perspectives and sensitivity analyses Permits transparency (eliminates the “black box”) Allows queuing (e.g., if a health resource is not available at a given time) Enables modeling of limited resources, bottlenecks, if applicable to the problem Defines patients as explicit elements with specific attributes (e.g, sex, age, eventhistory) that can be modified over time Provides option of updating variables continuously or at specific time periods In economic evaluations, DES model has the flexibility to accommodate a richerstructure without making it unmanageable in size16© GHEP 2013
  16. 16. © GHEP 2013Health Economic ModelingAdvantages of Discrete-Event SimulationReal-World SIMUL8 ModelQuestionsReferences17
  17. 17. © GHEP 2013Health Economic ModelingAdvantages of Discrete-Event SimulationReal-World SIMUL8 ModelQuestionsReferences18
  18. 18. Health Economic ModelingAdvantages of Discrete-Event SimulationReal-World SIMUL8 ModelReferencesQuestions
  19. 19. References1. Weinstein MC, Principles of good practice for decision analytic modeling in health-careevaluation: report of the ISPOR Task Force on Good Research Practices--Modeling Studies. ValueHealth. 2003, Jan-Feb;6(1):9-17.2. Sun X, Faunce T. Decision-analytical modelling in health-care economic evaluations. Eur J HealthEcon, 2008, 9:313-323.3. Siebert U, Alagoz O, Bayoumi AM, Jahn B, Owens K, Cohen D, Kunz KM. State- Transition Modeling:A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Value in Health, 2012,(15):812-820.4. Pidd M. Computer Simulation in Management Science (5th ed). New York: John Wiley & Sons, 2004.5. Jacobson SH, Hall SN, Swisher JR. Discrete-event simulation of health care systems, patient flow:reducing delay in healthcare delivery. Int Ser Oper Res Manag Sci 2006;91:211–52.6. Figge MT. Stochastic discrete event simulation of germinal center reactions. Phys Rev E Stat NonlinSoft Matter Phys 2005;71:1–9.7. Zand MS, Briggs BJ, Bose A, Vo T. Discrete event modeling of CD4 memory T cell generation. JImmunol 2004;173:3763–72.8. Coelli FC, Ferreira RB, Almeida RM, Pereira WC. Computer simulation and discrete-event models inthe analysis of a mammography clinic patient flow. Computer Methods Programs Biomed2007;87:201–7.
  20. 20. References9. Comas M, Castells X, Hoffmeister L, et al. Discrete-event simulation applied to the analysis ofwaiting lists: evaluation of a prioritization system for cataract surgery. Value Health 2008;11:1203–13.10. Stahl JE, Rattner D, Wiklund R, et al. Reorganizing the system of care surrounding laparoscopicsurgery: a cost-effectiveness analysis using discrete-event simulation. Med Decis Making2004;24:461–71.11. Clark DE, Hahn DR, Hall RW, Quaker RE. Optimal location for a helicopter in a rural trauma system:prediction using discrete-event computer simulation. Proc Annu Symp Comput Appl Med Care1994;888 –92.12. Chase D, Roderick P, Cooper K, et al. Using simulation to estimate the cost effectiveness ofimproving ambulance and thrombolysis response times after myocardial infarction. Emerg Med J2006;23:67–72.13. Skolnik JM, Barrett JS, Jayaraman B, et al. Shortening the timeline of pediatric phase I trials: therolling six design. J Clin Oncol 2008;26:190–5.14. Barth-Jones DC, Adams AL, Koopman JS. Monte Carlo simulation experiments for analysis of HIVvaccine effects and vaccine trial design. Winter Simul Conf Proc 2000;2:1985–94.15. Groothuis S, van Merode GG. Discrete event simulation in the health policy and managementprogram. Methods Inf Med 2000;39:339–42.
  21. 21. References16. Mar J, Arrospide A, Comas M. Budget impact analysis of thrombolysis for stroke in Spain: adiscrete event simulation model. Value Health 2010;13:69 –76.17. Stahl JE, Vacanti JP, Gazelle S. Assessing emerging technologies—the case of organ replacementtechnologies: volume, durability, cost. Int J Technol Assess Health Care 2007;23:331– 6.18. Caro JJ, Moller J, Getsios D. Discrete Event Simulation: The Preferred Technique for HealthEconomic Evaluations? Value in Health. 2010, Vol 13(8):1056-1060.19. Barton P, Bryan S, Robinson S (2004) Modelling in the economic evaluation of health care:selecting the appropriate approach. J Health Serv Res Policy 9: 110–118.20. Brennan A, Chick SE, Davies R. A taxonomy of model structures for economic evaluation ofhealth technologies. Health Econ. 15: 1295–1310 (2006).21. Cairo JJ. Pharmacoeconomic Analyses Using Discrete Event Simulation. Pharmacoeconomics2005; 23 (4): 323-332.22. Weinstein MC. Recent developments in decision-analytic modelling for economic evaluation.Pharmacoeconomics. 2006; 24(11):1043–53.23. Karnon J. Alternative decision modelling techniques for the evaluation of health caretechnologies: Markov processes versus discrete event simulation. Health Econ.2003;12(10):837–48.
  22. 22. References24. Kamon J, Brown J. Selecting a decision model for economic evaluation: a case study andreview. Health Care Management Science 1 (1998) 133–140.25. Simpson KN, Strassburger A, Jones WJ, Dietz B, Rajagopalan R. Comparison of Markov Modeland Discrete-Event Simulation Techniques for HIV. Pharmacoeconomics 2009; 27 (2): 159-165.
  23. 23. SIMUL8 Corporation | | info@SIMUL8.com1 800 547 6024 | +44 141 552 6888•••