1. Health care decision making
Dr. Giampiero Favato
presented at the University Program in Health Economics
Ragusa, June 2008

2. Health care decision making
Introduction to cost-effectiveness analysis
Combining costs and effects
Incremental ratios and decision rules
Beyond the ICER
Information for decision making
Trials vs. models
Introduction to decision analysis
Incorporating uncertainty

3. Forms of economic evaluation
Difference

4. Structure of economic evaluation
Standard treatment
New intervention
Resource use
Health outcomes
Health outcomes
Resource use
Physical quantities, QALYs, Monetary value
Total cost = resource use * unit cost
Physical quantities, QALYs, Monetary value
Total cost = resource use * unit cost
Benefit with standard treatment
Cost associated with standard treatment
Patient-specific benefit with new intervention
Patient-specific cost under new intervention
Cost-effectiveness analysis

5. Cost-effectiveness analysis
Mutually exclusive programmes
Incremental cost-effectiveness ratios
= ΔC = Cost new treatment – cost current treatment
ΔE Effect new treatment – effect current treatment
Decision rules
Independent programmes

6. (Strong) Dominance Programme Costs Effects Management of angina A B C20 30 50 60
110 8 4 19 23
Dominated: A has lower effects and higher cost than A
Management of angina

7. Average vs. incremental cost-effectiveness ratios
Programme
Costs
Effects A B C D E
Breast screening
110
120
150
190
240 20 29 50 60 70
C/E
ΔC/ΔE
5.50
4.14
3.00
3.17
3.42 -
1.11
1.43
4.00
5.00
Average ratios have no role in decision making

8. Incremental cost-effectiveness plane
New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
New treatment dominates
Old treatment dominates
New treatment more costly and more effective
New treatment less costly and less effective

9. Maximum acceptable ratio
New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
Maximum ICER

10. Cost analysis decision rule
Choose new technology (n) if:
ICER = Δ Costs < l
Δ Effects

11. Cost-effectiveness frontier – management of HIV
Difference in effects
Difference in costs A B D E

12. The cost-effectiveness plane

13. Maximum acceptable ratio
New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
Maximum ICER

14. Maximum acceptable ratio
When intervention more/less costly and more/less effective than comparator, cannot determine whether cost-effective unless use data from outside study
maximum acceptable ratio
Set by budget constraint
Set by maximum willingness to pay per unit of effect
Administrative ‘rule of thumb’
Empirically based

15. Cost effectiveness league tables
In recent years it has become fashionable to compare health care interventions in terms of their relative cost-effectiveness (incremental cost per life-year or cost per quality-adjusted life-year gained).
There are two, quite distinct, motivations behind the league table approach:
1. Analysts undertaking an evaluation of a particular health treatment or programme often seek, quite appropriately, to place their findings in a broader context.
2. Some analysts seek to inform decisions about the allocation of health care resources between alternative programmes. Most of the criticisms of league tables are directed at the second of these two potential motivations.

16. League table: an example

17. Grades of recommendation for adoption of new technologies
A: Compelling evidence for adoption
New technology is as effective, or more effective, and less costly
B: Strong evidence for adoption
New technology more effective, ICER ≤ $20,000/QALY
C: Moderate evidence for adoption
New technology more effective, ICER ≤ $100,000/QALY
D: Weak evidence for adoption
New technology more effective, ICER > $100,000/QALY
E: Compelling evidence for rejection
New technology is less effective, or as effective, and more costly

18. Grades of recommendation for adoption of new technologies II
New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly A B C D E

19. Trials and economic evaluation
Internal validity
External validity
Relevance
Inappropriate comparators
Limited follow-up
Surrogate/intermediate endpoints
Information synthesis
Uncertainty

20. Contrasting paradigms
Measurement
Testing hypotheses about individual parameters
Relatively few parameters of interest
Primary role for trials and systematic review
Focus on parameter uncertainty 
Decision making
What do we do now based on all sources of knowledge?
Decisions cannot be avoided
A decision is always taken under conditions of uncertainty
Decision making involves synthesis
Can be based on implicit or explicit analysis

21. What is a decision model?
Mathematical prediction of health-related events
Usually comparison of mutually exclusive interventions for a specific patient group
Events are linked to costs and health outcomes
Synthesise data from various sources
Uncertainty in data inputs
Focus on appropriate decision
Clinical versus economic

22. Key elements of models Models are simplified versions of reality
As simple/complex as required without losing credibility
Allow
Comparison of all feasible alternative interventions/strategies
Exploration of the full range of clinical policies
For range of patient sub groups
Systematic combination of evidence from variety sources

23. Data sources for modelling
Baseline event rates
Relative treatment effects
Long-term prognosis
Resource use
Quality of life weights (utilities)
Observational studies/trials
Trials
Longitudinal observational studies
Cross sectional surveys/trials
Type of parameter
Source

24. SIMPLE DECISION TREE Chance node Decision node ICER Side effect
Use adjuvant
No side effect
Chance node
Side effect
ICER
Don't use adjuvant
No side effect
Decision node

25. SIMPLE DECISION TREE ICER Side effect QALY 1 Cost 1 Use adjuvant
No side effect
QALY 2
Cost 1
QALYs adjuvant
Cost adjuvant
Side effect
ICER
QALY 1
Cost 2
QALYs no adjuvant
Cost no adjuvant
Don't use adjuvant
No side effect
QALY 2
Cost 2

26. Probability
Probability: a number between 0 and 1 expressing likelihood of an event over a specific period of time
Can reflect observed frequencies
Can reflect strength of belief
Sum of probabilities of mutually exclusive Events = 1
Joint probability: P(A and B)
Conditional probability: P(A/B)
P(A and B) = P(A/B) x P(B)

27. DECISION TREES: PREVENTION OF VERTICAL TRANSMISSION OF HIV
COSTS
PROBABILITY
Acceptance of interventions
p=0.07
£800
0.0665
p=0.95
No vertical transmission
Policy of
intervening
C=£800
p=0.93
£800
0.8835
Vertical transmission
No acceptance of interventions £0
p=0.26
0.013
p=0.05
No vertical transmission
C=£0 £0
0.037
p=0.74
Vertical transmission £0
0.26
p=0.26
Policy of not
intervening
No vertical transmission £0
0.74
p=0.74
Adapted from Ratcliffe et al. AIDS 1998;12:

28. Uncertainty Population Parameter Structural Sub-group analysis
Sensitivity analysis
Structural

29. Sensitivity analysis
Deterministic
One-way
Multi-way
Probabilistic

30. Model validation What are we validating? What do we validate against?
inputs
outputs
structure
mechanics/relationships
What do we validate against?
RCT results
Observational studies
all models are wrong, but some are useful