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

8

4

19

23

20

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





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 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


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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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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


p=0.26



p=0.26


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

health care decision making

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