This document introduces decision-analytic modeling techniques for clinical and economic projections. It discusses why modeling is useful, provides a taxonomy of modeling techniques including Markov models, and showcases examples of models that project clinical and economic outcomes. It concludes by guiding interested audience members to further information on modeling tools, societies, journals, and educational institutions.

1. Decision-analytic Modeling
for Clinical and Economic
Projections
Benjamin P. Geisler, M.D., M.P.H.
PGY-3 IM Resident, NYU School of Medicine
International Critical Care Data Mining Marathon
September 5, 2014
Cambridge, MA/London/Paris

2. Bayes’ Theorem

3. Objectives
• To convince you why decision-analytic modeling might
be useful
• To give you an overview over the “taxonomy” of
decision-analytic modeling techniques
• To illustrate one particular species, station transition or
“Markov” models
• To showcase examples that project clinical and
economic outcomes
• To guide interested audience members to further
information (modeling tools, societies/journals,
institutions/classes)

4. Outline
• Why Model?
• Taxonomy
• Markov spp.
• Showcase
• For further information, please dial …

5. Why Model?

7. Why Model? Flight Simulator

8. Why Model? Computer Models
Project Ebola Cases Project Federal HC Costs
Gomes et al. PLOS Currents Outbreaks. 2014 Sep 2. Ed 1 business-insider.com/Congressional Budget Office

9. Why Model? Cost/Benefit (1)
Design
phase
Implementation
phase
Operation
phase
System Costs
Cost without simulation
Cost with simulation
Harrel, Ghosn, Bowden; adapted from Stahl J

10. Why Model? Cost/Benefit (2)
• Concept = 1 x costs
• Design = 10 x concept error
• Implementation = 10 x design error
• Operation = 10 x implementation error
1 10
100
1000
CONCEPT DESIGN IMPLEMENTATION OPERATION
SYSTEM COST
Rule of Tens Cost Principle; adapted from Stahl J

11. No model is a perfect representation of reality;
its validity rests on whether assumptions are reasonable in light of
needs and purposes of the decision maker and, importantly, in light of
whether, after close examination its implications make sense.
Gold, Siegel, Russell, Weinstein 1996

12. Taxonomy

13. Taxonomy
• Mathematical and
statistical models
• Regression models,
“area under the curve”
• Decision trees
• Markov models
• Modifications incl.
“memory”
• Markov chains and
decision processes
• Sequential decisions
• Influence diagrams
• Causal inference
• Compartment models
• System dynamics
• Discrete event
simulations
• Queuing problems,
operations research
• Agent-based models
• Communicable diseases

14. DEEPP = Describe, Evaluate, Explore, Predict, and Persuade
Stahl J Pharmacoeconomics 2008;26(2):131-48
DES = Discrete Event Simulation
KISS = Keep It Simple Stupid

15. A B C D
Cohort/Aggregate Level/Counts Individual Level
Expected value,
Continuous state,
Deterministic
Markovian, Discrete State,
Stochastic
Markovian, Discrete State,
Individuals
Non-Markovian, Discrete-
State, Individuals
1
No Interaction
Untimed
Decision Tree Rollback Simulated Decision Tree
(SDT)
Individual Sampling Model (ISM):
Simulated Patient-Level Decision Tree (SPLDT)
2
Timed
Markov Model
(Evaluated
Deterministically)
Simulated Markov Model
(SMM)
Individual Sampling Model (ISM):
Simulated Patient-Level Markov Model (SPLMM)
(variations as in quadrant below for
patient level models with interaction)
3
Interaction
Discrete Time
System Dynamics (Finite
Difference Equations,
FDE)
Discrete Time Markov Chain
Model (DTMC)
Discrete-Time Individual
Event History Model
(DT, IEH)
Discrete Individual Simulation
(DT, DES)
Brennan A, Chick SE, and Davies R

16. Markov spp.

17. Markov spp: Decision Tree
Response
No Response
Treat
Response
No Response
Wait and see
Response
No Response
No Treat
Patient or
Population
p1
1-p1
p2
1-p2
p3
1-p3
Consequences
LYs QALYs $
LYs QALYs $
LYs QALYs $
LYs QALYs $
LYs QALYs $
LYs QALYs $
Adapted from Siebert U and Goehler A

18. Markov spp: Bubble Diagram
p = 0.65 p = 0.60
p = 0.30
Well Disease
U=1 U=0.6
Costs
p = 0.05 p = 0.40
Death
U=0
p = 1,00
Adapted from Siebert U and Goehler A

19. Markov spp: Tree Combination
Adapted from Siebert U and Goehler A

20. Markov Spp: Trace
0.65
0.30
0.60 0.40 1.00
0.05
after 1 year after 1 year:
Well Disease Dead
$, LYs, QALYs
Start Well Disease Dead
0.60 1.00 0.40
Well Disease Dead
etc.
after 2 years
0.65
0.30
0.05
after 2 years:
$, LYs, QALYs
Cumulative
$, LYs, QALYs
Adapted from Siebert U and Goehler A

21. With Intervention
0.60 1.00 0.40
0.60 1.00 0.40
21
Markov spp: Trace in Tree
Start
after 1 year
after 2 years
Without Intervention
Well Disease Death
0.65
0.60 0.40 1.00
Well Disease Death
0.60 1.00 0.40
Well Disease Death
etc.
0.30
0.05
0.65
0.30
0.05
$ (without intervention)
LYs (with intervention)
QALYs (without intervention)
Start
after 1 year
after 2 years
Well Disease Death
0.84
Well Disease Death
Well Disease Death
etc.
0.10
0.15
0.84
0.10
0.15
$ (with intervention)
LYs (with intervention)
QALYs (with intervention)
Adapted from Siebert U and Goehler A

22. Markov spp.
Cohort Simulation Monte Carlo Simulation
Sonnenberg FA et al. Med Decis Making 13(4):322-38

23. Showcase

24. Taksler GB et al. Ann Intern Med 159(3):161-168

25. Showcase: USPSTF Recs (2)
Taksler GB et al. Ann Intern Med 159(3):161-168

26. Showcase: USPSTF Recs (3)
Taksler GB et al. Ann Intern Med 159(3):161-168

27. Showcase: Incremental Cost-effectiveness
Ratio
ICER =
$ Strategy A - $ Strategy B
QALYs Strategy A - QALYs Strategy B
i.e., incremental $ per incremental QALY gained
High value Acceptable value Low value
Cost-saving $0 $50K $100K $150K $200K $300K
Cost per quality-adjusted life year (QALY)
Adapted from Institute for Clinical and Economic Review

28. Showcase: Probabilistic Sensitivity
Analysis
28
Costs
QALY
ICE Scatter Plot
Cost-effectiveness
acceptability curve

29. Showcase: Value of Information
Kotz J SciBX 4(22)

30. For further information,
please dial …

31. [further]: Modeling Tools
• Simulators
• TreeAge, Arena …
• Simulation languages
• WinBugs/OpenBugs,
SIMAN, SLAM, GPSS
• General purpose
• Excel
• C++, Java, Fortran …
Ease of use
Simulators
Simulation
Languages
Other
General Purpose
Generalizability
‘High’ level
‘Low’ level
Adapted from Stahl J

32. [further] Societies/Journals
• Society for Medical Decision Making
• Medical Decision Making
• Foundation for Shared Medical Decision Making
• Library of decision aids
• International Society for Pharmacoeconomics and
Outcomes Research
• Value in Health, books
• Institute for Operations Research and the
Management Sciences
• Operations Research

33. [further] Institutions/Classes
• MIT: Laboratory for Information and Decision Systems
• HSPH: Center for Health Decision Science
• RDS 280/282/284/285/286/288
• MGH: Institute for Technology Assessment & Institute
for Clinical and Economic Review
• Tufts: Center for Health Decision Science
• BU: School of Management, Health Policy Institute,
Program for the Management of Variability in Health
Care Delivery
• Brandeis: Schneider Institutes, Heller School
• London: LSE, STMH
• Paris: HEC, ESSEC

34. [further] Thank You
• For further information, please dial …
• Get in touch during the conference!
• ben.geisler@gmail.com
• @ben_geisler
• LinkedIn/Doximity