Health care decision making
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Transcript of Health care decision making
Health care decision makingDr. Giampiero Favatopresented at the University Program in Health EconomicsRagusa, 26-28 June 2008
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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
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Forms of economic evaluation
Cost-minimisation Multiple outcomes in natural unitsAssumes outcomes identical/very similarComparison 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: utilityGeneric outcome measure (eg. QALY)
Cost-benefit Monetary values (WTP)Considerable progress WTP, but controversialHuman capital / stated preferences (contingent valuation)
Analysis Outcome valuation
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Total cost = resource use *
unit cost
Physical quantities,
QALYs, Monetary value
Total cost = resource use * unit cost
Physical quantities, QALYs, Monetary
value
Benefit with standard treatment
Cost associated with standard
treatment
Patient-specific benefit with new
intervention
Patient-specific cost under new
intervention
Standard treatment
Health outcomes
New intervention
Health outcomesResource use Resource use
Cost-effectiveness analysis
Structure of economic evaluation
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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
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A
B
C
D
E
Programme Costs Effects
20
30
50
60
110
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4
19
23
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Dominated: A has lower effects and higher cost than A
Management of angina
(Strong) Dominance
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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
Average vs. incremental cost-effectiveness ratios
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New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
New treatment dominates
Old treatment dominatesNew treatment more costly and more effective
New treatment less costly and less effective
Incremental cost-effectiveness plane
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Maximum acceptable ratio
New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
Maximum ICER
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Choose new technology (n) if:
ICER = Δ Costs < Δ Effects
Cost analysis decision rule
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Difference in effects
Dif
fere
nce
in
co
sts
A
B
D
E
Cost-effectiveness frontier – management of HIV
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The cost-effectiveness plane
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Maximum acceptable ratio
New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
Maximum ICER
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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
Maximum acceptable ratio
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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.
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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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New treatment less effective
New treatment more effective
New treatment more costly
New treatment less costly
A
B
CD
E
Grades of recommendation for adoption of new technologies II
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Trials and economic evaluation
Internal validity
External validity
Relevance– Inappropriate comparators– Limited follow-up– Surrogate/intermediate endpoints– Information synthesis– Uncertainty
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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
Contrasting paradigms
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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
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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
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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
Observational studies/trials
Cross sectional surveys/trials
Type of parameter Source
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SIMPLE DECISION TREE
Use adjuvant
Don't use adjuvant
Side effect
Side effect
No side effect
No side effect
ICER
Decision node
Chance node
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SIMPLE DECISION TREE
QALY 1Cost 1
QALY 1Cost 2
QALY 2Cost 1
QALY 2Cost 2
Use adjuvant
Don't use adjuvant
Side effect
Side effect
No side effect
No side effect
QALYs adjuvantCost adjuvant
QALYs no adjuvantCost no adjuvant
ICER
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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)
Probability
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DECISION TREES: PREVENTION OF VERTICAL TRANSMISSION OF HIV
Acceptance of interventions
Vertical transmission
Policy ofintervening
Policy of notintervening
p=0.95
No acceptance of interventions
p=0.05
p=0.07
No vertical transmission
p=0.93
Vertical transmission
p=0.26
No vertical transmission
Vertical transmission
p=0.26
No vertical transmission
COSTS
C=£800
C=£0
PROBABILITY
p=0.74
p=0.74
£800 0.0665
£800 0.8835
£0 0.013
£0 0.037
0.26£0
£0 0.74
Adapted from Ratcliffe et al. AIDS 1998;12:1381-1388
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Population– Sub-group analysis
Parameter– Sensitivity analysis
Structural– Sensitivity analysis
Uncertainty
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Deterministic– One-way– Multi-way
Probabilistic
Sensitivity analysis
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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