1. Health care decision making Dr. Giampiero Favato presented at the University Program in Health Economics Ragusa, 26-28 June 2008

2. 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. 3 Forms of economic evaluation

4. 4 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 outcomes Resource use Cost-effectiveness analysis Structure of economic evaluation

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

6. 6 ABCDEABCDE ProgrammeCostsEffects 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

7. 7 ProgrammeCostsEffects ABCDEABCDE Breast screening 110 120 150 190 240 20 29 50 60 70 C/EΔC/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

8. 8 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 Incremental cost-effectiveness plane

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

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

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

12. 12 The cost-effectiveness plane

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

14. 14 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

15. 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. 16 League table: an example

17. 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. 18 New treatment less effective New treatment more effective New treatment more costly New treatment less costly A B C D E Grades of recommendation for adoption of new technologies II

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

20. 20 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

21. 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. 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. 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 Observational studies/trials Cross sectional surveys/trials Type of parameterSource

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

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

26. 26 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

27. 27 DECISION TREES: PREVENTION OF VERTICAL TRANSMISSION OF HIV Acceptance of interventions Vertical transmission Policy of intervening Policy of not intervening 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 £8000.0665 £800 0.8835 £0 0.013 £0 0.037 0.26 £0 0.74 Adapted from Ratcliffe et al. AIDS 1998;12:1381-1388

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

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

30. 30 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