simul8 seminar-june-19-2013 final
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© GHEP 2013
Health Economic Modeling
Advantages of Discrete-Event Simulation
Real World SIMUL8 Model
Questions
References
4
Health Economic Modeling
The International Society for Pharmacoeconomics
and Outcomes Research (ISPOR) Task Force on Good
Research Practices – Modeling Studies:
"[...] an analytic methodology that accounts for events over
time and across populations, that is based on data drawn
from primary and/or secondary sources, and whose
purpose is to estimate the effects of an intervention on
valued health consequences and costs.“1
Health Economic Modeling
The aim of health economic modeling is to generate expected values for the
clinical and economic effects of therapeutic alternatives
Health Economic Modeling
There are two quite distinct aspects of model-based
economic evaluation
1. First, it is necessary to produce the mean estimate of
cost-effectiveness (or other outcome measures) for a
given set of parameters (Type of Model)
2. Second is the issue of exploring the effects of uncertainty
in the model inputs (Sensitivity Analysis)
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Health Economic Modeling
Decision Trees
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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 being modeled (A). Individual models consider the experience of each patient individually, even if they report results at the level of the entire population. Each individual has unique characteristics, on the basis of which their individual course is modeled (B).
Individual-Based State Transition Model1 Cohort-Based State Transition Model1
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Health Economic Modeling
State Transition Models (No Interaction)
Markov and Monte Carlo Simulation2,3
Markov Model Adapted from Hepatitis B Model1
Monte Carlo Simulation Adapted from Hepatitis B Model1
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Health Economic Modeling
Event-Based Models (Interaction)
Discrete-Event Simulation (DES)
Discrete event simulation (DES) is a flexible modeling method characterized by the ability to represent complex behavior within, and interactions between individuals, populations, and their environment4
Applications in health care have increased over the last 40 years5 and include biologic models6,7, process redesign and optimization8–10, geographic allocation of resources11,12, trial design13,14, and policy evaluation15–17
In health economics, DES is a preferred choice as it favors greater flexibility in depicting the cost-effectiveness of prevention or therapeutic interventions for chronic disease18
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Health Economic Modeling
Basic Patient/Disease Pathway – Chronic Venous Leg Ulcer
© GHEP 2013 12
Health Economic Modeling
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© GHEP 2013
Health Economic Modeling
Advantages of Discrete-Event Simulation
Real-World SIMUL8 Model
Questions
References
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 wounds separately, which requires a large number of attributes and events that likely exceeds the 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 previous attribute).
Such “memories” can be attached to the individuals in a DES model, which is difficult to achieve with a cohort Markov approach
In a DES model, individual life histories are created by drawing randomly from distributions that describe the time to the occurrence of particular events. The individuals from the study population would move from one attribute to another, driven by events, by means of time to progression of wound severity, time to decision for hospitalization, probability of infection and death, survival time of wounds to healing, and time to death.
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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 of time (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, event history) 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 richer structure without making it unmanageable in size
16 © GHEP 2013
© GHEP 2013
Health Economic Modeling
Advantages of Discrete-Event Simulation
Real-World SIMUL8 Model
Questions
References
17
© GHEP 2013
Health Economic Modeling
Advantages of Discrete-Event Simulation
Real-World SIMUL8 Model
Questions
References
18
Health Economic Modeling
Advantages of Discrete-Event Simulation
Real-World SIMUL8 Model
References
Questions
References
1. Weinstein MC, 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, Jan-Feb;6(1):9-17.
2. Sun X, Faunce T. Decision-analytical modelling in health-care economic evaluations. Eur J Health Econ, 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 Nonlin Soft Matter Phys 2005;71:1–9.
7. Zand MS, Briggs BJ, Bose A, Vo T. Discrete event modeling of CD4 memory T cell generation. J Immunol 2004;173:3763–72.
8. Coelli FC, Ferreira RB, Almeida RM, Pereira WC. Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow. Computer Methods Programs Biomed 2007;87:201–7.
References
9. Comas M, Castells X, Hoffmeister L, et al. Discrete-event simulation applied to the analysis of waiting 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 laparoscopic surgery: a cost-effectiveness analysis using discrete-event simulation. Med Decis Making 2004;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 Care 1994;888 –92.
12. Chase D, Roderick P, Cooper K, et al. Using simulation to estimate the cost effectiveness of improving ambulance and thrombolysis response times after myocardial infarction. Emerg Med J 2006;23:67–72.
13. Skolnik JM, Barrett JS, Jayaraman B, et al. Shortening the timeline of pediatric phase I trials: the rolling six design. J Clin Oncol 2008;26:190–5.
14. Barth-Jones DC, Adams AL, Koopman JS. Monte Carlo simulation experiments for analysis of HIV vaccine 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 management program. Methods Inf Med 2000;39:339–42.
References
16. Mar J, Arrospide A, Comas M. Budget impact analysis of thrombolysis for stroke in Spain: a discrete event simulation model. Value Health 2010;13:69 –76.
17. Stahl JE, Vacanti JP, Gazelle S. Assessing emerging technologies—the case of organ replacement technologies: 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 Health Economic 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 of health technologies. Health Econ. 15: 1295–1310 (2006).
21. Cairo JJ. Pharmacoeconomic Analyses Using Discrete Event Simulation. Pharmacoeconomics 2005; 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 care technologies: Markov processes versus discrete event simulation. Health Econ. 2003;12(10):837–48.
References
24. Kamon J, Brown J. Selecting a decision model for economic evaluation: a case study and review. Health Care Management Science 1 (1998) 133–140.
25. Simpson KN, Strassburger A, Jones WJ, Dietz B, Rajagopalan R. Comparison of Markov Model and Discrete-Event Simulation Techniques for HIV. Pharmacoeconomics 2009; 27 (2): 159-165.
