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CAPHRI School for Public Health and Primary Care
Introduction to decision analytic modelling
Prof. Dr. Manuela Joore Dept. Clinical Epidemiology & MTA, Maastricht UMC+, m.joore@mumc.nl
EUHEA pre-conference workshop July 11th 2018
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CAPHRI School for Public Health and Primary Care
Outline • Decision analytic modelling for health
economic evaluation: – What is it & why use it?
– Types/Approaches
– Components
– Analyses
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CAPHRI School for Public Health and Primary Care
What is a decision analytic model? (for health economic evaluation)
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CAPHRI School for Public Health and Primary Care
What is a definition of ‘model’?
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CAPHRI School for Public Health and Primary Care
What is a definition of ‘model’?
A model is a simplified reflection
of reality
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CAPHRI School for Public Health and Primary Care
Can you think of examples of models?
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CAPHRI School for Public Health and Primary Care
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CAPHRI School for Public Health and Primary Care Δ
€
Δ QALYs
Health economic evaluation: two approaches
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CAPHRI School for Public Health and Primary Care Δ
€
Δ QALYs
Health economic evaluation: two approaches
R R
Intervention A
Intervention B
€, QALYs
€, QALYs
Mo
de
l Tri
al
Mild Severe
Death
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CAPHRI School for Public Health and Primary Care
Why model?
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CAPHRI School for Public Health and Primary Care
Policy decisions
Scientific knowledge
Health economic decision modelling
RCTs, cohort studies / registries, surveys, hospital information, price information, expert opinion...
Is A cost-effective compared to B (C, D, …)
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CAPHRI School for Public Health and Primary Care
Model based economic evaluation: Why?
1. Extending results from a trial = extrapolation
2. Combining / synthesizing available evidence
3. Generalising trials into practice (location, population, …)
4. Modelling prior to trials and studies
5. Modelling to address uncertainty
population S
?
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CAPHRI School for Public Health and Primary Care
Components in modelling
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CAPHRI School for Public Health and Primary Care
Components in modelling (see ISPOR/SMDM taskforce papers, Value in Health 2012 vol 15: p 796 etc.)
1. Conceptualising a model
2. Specific modeling techniques
3. Parametrizing a model
4. Handling uncertainty
5. Model validation & transparency
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CAPHRI School for Public Health and Primary Care
1. Conceptualising a model (ISPOR/SMDMD taskforce 2)
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CAPHRI School for Public Health and Primary Care
1. Conceptualising a model
1. Model should match the decision, not vice versa
2. Specify the decision problem
– population, intervention, comparators, outcomes, time horizon, perspective, audience)
3. Conceptual structure of the model driven by decision problem not by data availability
4. Conceptual model should be able to indentify key uncertainties
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CAPHRI School for Public Health and Primary Care
Components in modelling (see ISPOR/SMDM taskforce papers, Value in Health 2012 vol 15: p 796 etc.)
1. Conceptualising a model
2. Specific modeling techniques
3. Parametrizing a model
4. Handling uncertainty
5. Model validation & transparency
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CAPHRI School for Public Health and Primary Care
Modeling approaches
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CAPHRI School for Public Health and Primary Care
Modeling approaches
• Technique should match conceptual model
– Decision tree • No explicit modeling of time, easy/messy
– State transition model • Widely used
– Discrete event simulation • Constrained resources / interaction between individuals / time-
to-event data / need to record individual experience
– Dynamic transmission model • Infectious disease
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CAPHRI School for Public Health and Primary Care
Decision tree
Option A
Option B
Healthy
Disease
Death
Healthy
Disease
Death
Healthy
Disease
Death
IDEM
0.6
0.1
0.3
Etc.
Etc.
Etc.
Etc.
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CAPHRI School for Public Health and Primary Care
State transition model (ISPOR/SMDM taskforce 3)
• Conceptualizes a decision problem in terms of – mutually exclusive and collectively exhaustive health states
– transitions among those states
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CAPHRI School for Public Health and Primary Care
State transition model: mutually exclusive & collectively exhaustive health states & transitions between those states
• Health states
• Transitions
• Utilities & Costs
– Health states
– Transitions
Alive
Death
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CAPHRI School for Public Health and Primary Care
State transition model: mutually exclusive & exhaustive health states
• Population with disease X, mild stage
• Patients may progress to severe and have increased mortality risk, but may also improve
• New treatment may decrease probability of progression and mortality
• Can you think of a model structure?
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CAPHRI School for Public Health and Primary Care
State transition model: mutually exclusive & exhaustive health states
Healthy Disease
Death
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CAPHRI School for Public Health and Primary Care
State transition model: comparing health care technologies
Healthy Disease
Death
Healthy Disease
Death
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CAPHRI School for Public Health and Primary Care
State transition model: Timing - discrete
Healthy Disease
Death
Healthy Disease
Death
Healthy Disease
Death
Cycle 0
Cycle 1
Cycle 2
Etcetera Time horizon
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CAPHRI School for Public Health and Primary Care
State transition model (ISPOR/SMDM taskforce 3)
• Conceptualizes a decision problem in terms of – mutually exclusive and collectively exhaustive health states
– transitions among those states
• Takes timing into account
• Suitable to model the dynamics of disease and intervention (recurrent events)
• Markovian property (hence “Markov model”) – transition probabilities do not depend on history neither on past
states nor on the time spent in the current state
– very limiting!
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CAPHRI School for Public Health and Primary Care
Training 1 – conceptualising a model
• 20 minutes!
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CAPHRI School for Public Health and Primary Care
Manuela Joore
Professor HTA & Decision Making Deputy Head Department Clinical Epidemiology & Medical Technology Assessment
Maastricht University Hospital PO Box 5800
6202 AZ The Netherlands m.joore@mumc.nl
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CAPHRI School for Public Health and Primary Care
State transition model: mutually exclusive & exhaustive health states
Healthy Disease
Death
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CAPHRI School for Public Health and Primary Care
Training 1
• Time horizon:
• Modelling approach:
• Structure:
• What types of information do you need?
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