Transcript No Slide Title

Health care decision making
Dr. Giampiero Favato
presented at the University Program in Health Economics
Ragusa, 26-28 June 2008
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
2
Forms of economic evaluation
Analysis
3
Outcome valuation
Cost-minimisation
Multiple outcomes in natural units
Assumes outcomes identical/very similar
Comparison of costs
Cost-effectiveness
Cost per unit of effect
Single outcome, common effect; natural units:
- Intermediate (e.g. blood pressure)
- Final (e.g. LYG)
Cost-utility
Broader measure of benefitis: utility
Generic outcome measure (eg. QALY)
Cost-benefit
Monetary values (WTP)
Considerable progress WTP, but controversial
Human capital / stated preferences (contingent valuation)
Structure of economic evaluation
Standard treatment
Health outcomes
Physical
quantities,
QALYs,
Monetary value
Benefit with
standard
treatment
Resource use
Total cost =
resource use *
unit cost
Cost associated
with standard
treatment
New intervention
Health outcomes
Physical quantities,
QALYs, Monetary
value
Patient-specific
benefit with new
intervention
Cost-effectiveness analysis
4
Resource use
Total cost = resource
use * unit cost
Patient-specific cost
under new
intervention
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
5
(Strong) Dominance
Management of angina
Programme Costs
Effects
A
6
20
8
B
30
4
C
50
19
D
60
23
E
110
20
Dominated: A has
lower effects and
higher cost than A
Average vs. incremental cost-effectiveness
ratios
Breast screening
Programme Costs
Effects C/E
ΔC/ΔE
A
110
20
5.50
-
B
120
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4.14
1.11
C
150
50
3.00
1.43
D
190
60
3.17
4.00
E
240
70
3.42
5.00
Average ratios have no role in decision making
7
Incremental cost-effectiveness plane
New treatment
more costly
Old treatment dominates
New treatment more
costly and more
effective
New treatment
less effective
New treatment less
costly and less effective
New treatment
more effective
New treatment dominates
New
treatment
less costly
8
Maximum acceptable ratio
New
treatment
more costly
Maximum ICER
New treatment
more effective
New treatment
less effective
9
New
treatment
less costly
Cost analysis decision rule
Choose new technology (n) if:
ICER = Δ Costs
Δ Effects
10
<l
Cost-effectiveness frontier – management of
HIV
Difference in costs
E
D
B
A
Difference in effects
11
The cost-effectiveness plane
12
Maximum acceptable ratio
New
treatment
more costly
Maximum ICER
New treatment
more effective
New treatment
less effective
13
New
treatment
less costly
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
14
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 lifeyear 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.
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League table: an example
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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
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Grades of recommendation for adoption of new
technologies II
New
treatment
more costly
D
C
E
New treatment
less effective
B
A
New
treatment
less costly
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New treatment
more effective
Trials and economic evaluation
 Internal validity
 External validity
 Relevance
–
–
–
–
–
19
Inappropriate comparators
Limited follow-up
Surrogate/intermediate endpoints
Information synthesis
Uncertainty
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
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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
21
Key elements of models
 Models are simplified versions of reality
 As simple/complex as required without losing credibility
 Allow
–
–
–
–
22
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
Data sources for modelling
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Type of parameter
Source
Baseline event rates
Observational studies/trials
Relative treatment effects
Trials
Long-term prognosis
Longitudinal observational studies
Resource use
Observational studies/trials
Quality of life weights (utilities)
Cross sectional surveys/trials
SIMPLE DECISION TREE
Side effect
Use
adjuvant
No side effect
Chance node
Side effect
ICER
Don't use
adjuvant
Decision node
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No side effect
SIMPLE DECISION TREE
Side effect
Use
adjuvant
No side effect
QALYs adjuvant
Cost adjuvant
Side effect
ICER
QALYs no adjuvant
Cost no adjuvant
Don't use
adjuvant
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No side effect
QALY 1
Cost 1
QALY 2
Cost 1
QALY 1
Cost 2
QALY 2
Cost 2
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)
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DECISION TREES: PREVENTION OF
VERTICAL TRANSMISSION OF HIV
Acceptance of
interventions
p=0.95
Policy of
intervening
C=£800
No acceptance
of interventions
p=0.05
C=£0
Vertical transmission
COSTS PROBABILITY
p=0.07
p=0.93
£800
0.8835
£0
0.013
£0
0.037
£0
0.26
£0
0.74
Vertical transmission
p=0.26
No vertical transmission
p=0.74
Vertical transmission
No vertical transmission
p=0.74
Adapted from Ratcliffe et al. AIDS 1998;12:1381-1388
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0.0665
No vertical transmission
p=0.26
Policy of not
intervening
£800
Uncertainty
 Population
– Sub-group analysis
 Parameter
– Sensitivity analysis
 Structural
– Sensitivity analysis
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Sensitivity analysis
 Deterministic
– One-way
– Multi-way
 Probabilistic
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Model validation
 What are we validating?
–
–
–
–
inputs
outputs
structure
mechanics/relationships
 What do we validate against?
– RCT results
– Observational studies
all models are wrong, but some are useful
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