1. Introduction to decision analysis Jouni Tuomisto THL

2. Decision analysis is done for purpose: to inform and thus improve action QRAQRA

3. Decisions by an individual vs. in a society In theory, decision analysis is straightforward with a single decision-maker: she just has to assess her subjective probabilities and utilities and maximize expected utility. In practice, there are severe problems: assessing probabilities and utilities is difficult. However, in a society things become even more complicated: –There are several participants in decision-making. –There is disagreement about probabilities and utilities. –The decision models used are different. –The knowledge bases are different. NOTE! In this course, "knowledge" means both scientific (what is?) and ethical (what should be?) knowledge.

4. Probability of an event x If you are indifferent between decisions 1 and 2, then your probability of x is p=R/N. p 1-p Red x does not happen x happens White ball Decision 1 Red ball Decision 2 Prize 100 € 0 € 100 € 0 €

5. Outcome measures in decision analysis

6. –DALY: disability-adjusted life year –QALY: quality-adjusted life year –WTP: willingness to pay –Utility

7. Disability-adjusted life year –The disability-adjusted life year (DALY) is a measure of overall disease burden, expressed as the number of years lost due to ill-health, disability or early death. (Wikipedia)disease burden –Originates from WHO to measure burden of disease in several countries in the world.

8. DALYs in the world 2004 –Source: Wikipedia

9. How to calculate DALYs –DALY= YLL+YLD –YLL=Years of life lost –YLD=Years lived with disability –YLD = #cases*severity weight*duration of disase –More DALYs is worse.

10. Disability weights http://en.opasnet.org/w/Disability_weights

11. Weighting of DALYs –Discounting –present value W t = W t+n *(r+1) -n –Where W is weight, r is discount rate, and n is number of years into the future and t is current time –Typically, r is something like 3 %/year. –Age weighting –W = 0.1658 Y e -0.04 Y –where W is weight and Y is age in years

12. Discounting W t = W t+n (1+r) -n

13. Age weighting with DALYs W = 0.1658 Y e -0.04 Y

14. Estimating QALY weights Time-trade-off (TTO): Choose between:Time-trade-off –remaining in a state of ill health for a period of time, –being restored to perfect health but having a shorter life expectancy. Standard gamble (SG): –Choose between: –remaining in a state of ill health for a period of time, –a medical intervention which has a chance of either restoring them to perfect health, or killing them. Visual analogue scale (VAS): Rate a state of ill health on a scale from 0 to 100, with 0 representing death and 100 representing perfect health.Visual analogue scale

15. QALY weight of disease x (standard gamble) Adjust u in such a way that you are indifferent between decisions 1 and 2. Then, your QALY weight is u(x). u 1-u Dead Healthy Live with disease Disease Treatment Utility ? 0 1

16. Standard descriptions for QALYs E.g. as the EuroQol Group's EQ5D questionnaireEuroQol GroupEQ5D Categorises health states according to the following dimensions: –mobility, –self-care, –usual activities (e.g. work, study, homework or leisure activities), –pain/discomfort –anxiety/depression.

17. Measuring utilities Adjust u in such a way that you are indifferent between the two options. Then, your utility for option x is u(x). u 1-u Worst outcome Best outcome Choose option x Option Choose gamble Utility ? 0 1

18. Utility of money is not linear

19. CAFE clean air for Europe

20. Value of statistical life VSL Measure the willingness to accept slightly higher mortality risk. –E.g. a worker wants 50 € higher salary per month as a compensation for a work which has 0.005 chance of fatal injury in 10 years. –50 €/mo*12 mo/a*10 a / 0.005 = 1200000 € / fatality VSL is the marginal value of a small increment in risk. Of course, it does NOT imply that a person’s life is worth VSL. A similar measure: VOLY = value of life year.

21. The ultimate decision criterion: expected utility Max(E(u(d j )))=Max j (∑ i u(d j,θ i ) p(θ i ) ) Calculate the expected utility for each decision d option j. Pick the one with highest expected utility.

22. Which option is the best? 0.03 0.15 Healthy Swine flu Side effect Vaccination Swine flu Do nothing Utility 0.3 1 0 Healthy 0.003 1

23. Which option is the best? u(Vaccination)=0.976 u(Do nothing)=0.895  Choose vaccination 0.03 0.15 Healthy Swine flu Side effect Vaccination Swine flu Do nothing u; E(u) 0.3;0.009 1;0.85 0.3;0.045 0;0 Healthy 0.003 1;0.967

24. Limitations of decision trees A decision tree becomes quickly increasingly complex. This only contains two uncertain variables and max three outcomes of a variable. 0.03 0.15 Healthy Vaccination Swine flu Do nothing Complicat ions 0.003 Swine flu Complicat ions

25. Swine flu Causal diagrams: a powerful tool for describing decision analysis models Vaccination Outcome Complicat ions

26. Bayesian belief networks Arrows are causal dependencies described by conditional probabilities. P(swine flu | vaccination) P(complications | swine flu) P(outcome | swine flu, complicatons)  These probabilities describe the whole model.

27. Functional models Arrows are causal dependencies described by (deterministic) functions. swine flu = f1(vaccination) complications = f2(swine flu) outcome = f3(swine flu, complicatons)  These functions describe the whole model.

28. Functional vs. probabilistic dependency Va1=2.54*Ch1^2 Va2=normal(2.54*Ch1^2,2)

29. Estimating societal costs of health impacts In theory, all costs should be estimated. In practice, the main types considered include 1.Health case costs (medicine, treatment…). 2.Loss of productivity (absence from work, school). 3.WTP of the person to avoid the disease. The societal cost of disease to other people (relatives etc) is NOT considered.

30. St Petersburg paradox Consider the following game of chance: you pay a fixed fee to enter and then a fair coin is tossed repeatedly until a tail appears, ending the game. The pot starts at 1 dollar and is doubled every time a head appears. You win whatever is in the pot after the game ends. Thus you win 1 dollar if a tail appears on the first toss, 2 dollars if a head appears on the first toss and a tail on the second, 4 dollars if a head appears on the first two tosses and a tail on the third, 8 dollars if a head appears on the first three tosses and a tail on the fourth, etc. In short, you win 2 k−1 dollars if the coin is tossed k times until the first tail appears.game of chancetossed What would be a fair price to pay for entering the game? Solved by Daniel Bernoulli, 1738

31. St Petersburg paradox (2) To answer this we need to consider what would be the average payout: With probability 1/2, you win 1 dollar; with probability 1/4 you win 2 dollars; with probability 1/8 you win 4 dollars etc. The expected value is thusexpected value

32. Example of a model with causal diagram Dampness and asthma