Outcomes in Decision Analysis: Utilities, QALYs, and Discounting

1. Outcomes in Decision Analysis: Utilities, QALYs, and Discounting Aaron B. Caughey, MD, PhD abcmd@berkeley.edu Associate Professor in Residence Director, Center for Clinical and Policy Perinatal Research Department of Obstetrics and Gynecology University of California, San Francisco January 14, 2010

2. Disclosures • No personal financial disclosures • Research Funding: • NIH/NICHD • AHRQ – Elective Induction of Labor • Robert Wood Johnson Foundation – • Cesarean Delivery: Outcomes, Preferences, Costs • Hellman Foundation

3. Overview Back to the aneurysm example: To Clip Or Not To Clip? Clinical Outcomes Utilities and utility measurement Standard Gamble Time Tradeoff Calculating quality-adjusted life years Discounting

4. Review—Last Lecture Formulated an explicit question “to clip or not to clip” (incidental aneurysm ) Made a simple decision tree Conducted an expected value calculation to determine which course of action would likely yield the highest life expectancy

5. To Clip or Not To Clip =.9825 =.9921 =.55 =1.0 =.55 =.977 Diff = -0.0151 =0 .865 vs .977

6. To Clip or not to Clip? • Has an impact on life expectancy • Also actual clinical outcomes: • Surgical death • Aneurysm rupture • Death from aneurysm rupture • Neurologic Injury • Major • Minor • Fear of aneurysm rupture

7. Quantifying Health Outcomes • Mortality • Life Years number of expected years of life • Significant Morbidity Paralysis, loss of sight • Quality Adjusted Life Years Expected life years adjusted for the valuation of the possible states in each year • Financial Valuation of these Outcomes Costs to patient, payor, or society Willingness to pay to avoid outcomes, obtain treatment

8. Health Outcomes – Mortality • Mortality Death from disease/accident/procedure e.g. If Ms. Brooks undergoes surgery, one of the possible outcomes is mortality • Life Years Calculate an expected value of life years using a probabilistically weighted average of expected life e.g. If Ms. Brooks does not undergo surgery, her life expectancy is less than if she did not have aneurysm, these outcomes are measured in expected life years

9. Health Outcomes – Morbidity • Morbidity Some health state that is less than perfect e.g. disability from stroke, chronic pain • Comparison of morbidities Difficult – apples and oranges problem e.g. which is worse: Blind v. Deaf Deaf v. Paraplegia Paraplegia v. Blind

10. To Clip or not to Clip? • Clinical outcomes for clinician readers • Outcomes may affect health-related quality of life: how do we compare? • Neurologic injury can cause mild/moderate disability • Not clipping can cause anxiety associated with being at risk of aneurysm rupture • Outcomes may occur at different times

11. How do we incorporate quality-of-life effects into DA? • Measure/estimate and apply utilities • Use utilities to quality-adjust life expectancy for decision and cost-effectiveness analysis

12. Preview—Where We Are Going with this Analysis? Recall Ms. Brooks and her incidental aneurysm -- to clip or not to clip? We want to: Determine her utilities Use them to generate QALYs Evaluate incremental QALYs and cost (CEA/CUA) Compare incremental cost effectiveness ratios (ICER) to other currently accepted medical interventions

13. What is a Utility? Utility - Quantitative measure of the strength of an individual’s preference for a particular health state or outcome. Utilities can be obtained for: * Disease states (diabetes, depression) * Treatment effects (cure, symptom management) * Side effects (impotence, dry mouth) * Process (undergoing surgery, prenatal diagnostic procedure)

14. Utilities Utilities are the currency we use to assign values to outcomes Scaled from 0 to 1 1 = perfect or ideal health or health in the absence of the condition being studied 0 = death

15. How are utilities measured? Utilities are commonly estimated using comparisons to the 0 and 1 anchors Visual Analog Scale Standard Gamble Time Trade-off

16. BKA vs. AKA Example Patient in the hospital has infection of the leg Two options: 1) BKA BKA –1% mortality risk 2) Medical management – 20% chance of infection worsening and needing AKA AKA – above the knee amputation 10% mortality risk Let’s draw a decision tree

17. For which outcomes do we need to measure utilities? • Death? • Risk of worsening? • Living with part of a leg (below the knee) missing? • Living with a bigger part of a leg (above the knee) missing? • Others?

18. Visual Analog Scaling Full health: intact leg 100 98 99 65 BKA 55 AKA 2 1 Dead 0 Outcomes rated on a 0-to-100 “feeling thermometer.”

19. Standard Gamble What chance of immediate death would you be willing to incur to avoid living with the outcome being assessed? Method relies on respondents choosing between: 1) a certain outcome (BKA) 2) a gamble between an ideal outcome (intact leg) and the worst outcome (dead)

20. Standard Gamble Question Death Perfect Health

21. Standard Gamble Exercise Which do you prefer? Choice A Choice B Spend the rest of your life with BKA [p]% chance of immediate death 1-[p]% chance of spending the rest of your life with an intact leg

22. Standard Gamble • Standard gamble measurement involves questioning patients to determine the p at which the two outcomes are equivalent • Using expected utilities, the value of p gives the utility • Utility (BKA) x Prob (BKA) = Utility(cure) x (p) + Utility(death) x (1-p) • The utility of BKA = p: note P(BKA) = 1 • Utility (BKA) = [Utility(cure) x (p) + Utility(death) x (1-p)] • = [1.0 x p + 0 x (1-p)] = p

23. Time Tradeoff How many years of your life would you be willing to give up to spend your remaining life without the condition/health state being assessed? Method relies on respondents choosing between: 1) Full life expectancy with the condition/outcome being assessed (BKA) 2) A reduced life expectancy with the ideal outcome (intact leg)

24. Time Tradeoff Preference Elicitation Which do you prefer? Choice A Choice B Spend the remaining 40 years of your life with BKA Live 40 more years of life with an intact leg (give up 0 years of life)

25. Time Tradeoff Preference Elicitation Which do you prefer? Choice A Choice B Spend the remaining 40 years of your life with BKA Live 30 more years of life with an intact leg (give up 10 years of life)

26. Utility Measurement – Time Trade-off Time Trade-off involves patients choosing between: quality of life v. length of time alive When patients are equivocal between choice: Time A * Utility A = Time B * Utility B e.g. If you have a life expectancy of 30 years with a BKA; how much time would you give-up to live in your current state? Would you give up 5 years? 3 years? 1 year? 30 years * Utility (BKA) = (30-x) years * 1.0 If you’re willing to give up 3 years, that means: Utility of BKA = [(30-3)*1/ 30] = 27/30 = 0.9

27. Pros and Cons - VAS Advantage: Easy to understand Disadvantages: Doesn’t require the respondent to: Think about what they’d be willing to give up Explore risk preference Values spread over the range

28. Pros and Cons – SG Advantages: Requires assessor to give something up, incorporates risk attitude Disadvantages: Choices may be difficult to make Most confusion-prone method Lack of engagement or willingness to participate in exercise Values tend to cluster near 1

29. Pros and Cons – TTO Advantages: Still asking assessor to give something up Easier choices than SG. Values not so clustered near 1 Disadvantages: Fails to incorporate risk Lack of clarity of when time traded occurs Isn’t something that one can choose to give up. (One can take on a risk of death, but not “pay with life years.”)

30. Utilities in decision analysis Utilities can adjust life expectancy in DA where outcomes include morbidity/quality-of-life effects. Quality Adjusted Life-Years (QALYs)

31. QALYs QALYs are generally considered the standard unit of comparison for outcomes QALYs = time (years) x quality (utility) e.g. 40 years life expectancy after AKA, utility (AKA) = 0.9 = 40 x 0.9 = 36 QALYs

32. Back to aneurysm

33. =.9825 =.9921 =.55 =1.0 =.55 =.977 Diff = -0.0151 =0 .865 vs .977

34. Now we want to add utilities for intermediate outcomes

35. Including utility for early death and stroke=0.5

36. Adding utility for worry =.95

37. Outcomes - Discounting • “Men often, from infirmity of character, make their election for the nearer good, though they know it to be the less valuable”* *Mill JS. Utilitarianism. London: Routledge, 1871

38. Outcomes - Discounting Aneurysm Example We said since life expectancy is reduced by 2/3, so instead of 35, it is = 35 * .333 = 11.67 However, are all years considered equal? Consider: Favorite Meal Extreme Pain Lifetime Income

39. Outcomes - Discounting Generally, present > future One common way to value the different times is discounting Essentially this year is worth δ more than next year δ is commonly set at 0.03 or 3% In order to compare values of all future times, a calculation, net present value, is often used NPV = 1 / (1 + δ)t Where t is number of years in the future

40. Outcomes - Discounting Aneurysm Example If utility is 0.6 and life expectancy is 3 years NPV would be:  Utility / (1 + δ)t However, when is year 1? Often, since events in year one occur on average half way through, we use 0.5 for year 1 NPV = 0.6 / (1.03)0.5 + 0.6 / (1.03)1.5 + 0.6 / (1.03)2.5 NPV = 0.6 * {(1.03)-0.5 + (1.03) -1.5 + (1.03) -2.5}

41. QALYs disc No aneurysm rupture Normal survival, 21.4 worry 0.9825 No surgery 21.37 Die Early death, 13.3 Aneurysm rupture worry 0.45 Survive Normal survival, 0.0175 21.4 worry 0.55 Ms. Brooks No aneurysm rupture Normal survival 21.5 Difference 1 Δ QALYs -1.63 Survive surgery Die 0.902 Early death 13.4 Aneurysm rupture 0.45 Clipping Survive 0 Normal survival 21.5 19.74 0.55 Key Inputs Surgery-induced disability Disability, 4.8 Rupture risk/yr 0.0005 shorter survival 0.075 Expected life span 35 RR rupture w/ surgery 0 Surgical death Immediate death 0.0 Surgical mortality 0.023 0.023 Surg morb (disability) 0.075 Outcomes - Discounting

42. Exponential Discounting • Exponential discounting first described in 1937* • Mathematically easy to manipulate • Assumed discounting in “simple regular fashion” • Does not differentiate difference between: • Today vs. tomorrow • Ten years vs. ten years plus one day *Samuelson PA. A Note on Measurement of Utility. Rev Econ Stud 1937;4:155-61

43. Discounting – Special Topic Think about your favorite dessert. How much would you pay to have now? How much would pay to have tonight? How much would you pay to have in 1 yr? How much would you pay in 1 yr and 1 day?

44. Exponential DiscountingProblems with the Model • Discounting unlikely to be constant • Anticipal effect is not demonstrated • Difference in valuations appears greater when closer • Discount reversal effects not incorporated • Far future, prefer A to B • Near future, prefer B to A

45. Discounting – Special Topic Solutions: Measure discount rates through life Could model with present-biased preferences Essentially, “today” versus all other time periods is valued higher for many outcomes Difference in future outcomes is likely similar

46. Present-Biased Preferences • Described by: • Phelps and Pollack in 1968* • O’Donoghue and Rabin in 1999** • Two parameter model***: • β – the difference between today and “tomorrow” • δ – the difference between all future time intervals • Model accounts for • Discount reversal effects • Component of anticipal effects *Phelps ES, Pollack RA. On Second-Best National Saving and Game-Equilibrium Growth. Rev Econ Studies 1968;35:185-99 **O’Donoghue T, Rabin M. Doing it Now or Later. Amer Econ Rev 1999;89:103-124 *** Laibson D. Golden Eggs and Hyperbolic Discounting. QJE 1997;112:443-77

47. Exponential vs. PBP Exponential: UT = UP(outcome) + Σn δn UP(outcome) Present-biased preferences: UT = UP (outcome) + β[Σn δn UP (outcome)] UT is the total NPV utility UP is the moment to moment utility β gives difference between immediate and all other time periods, while δ is difference in the future

48. Discounting: Prescriptive vs. Descriptive • We discount • But, should we • Example - perceived time

49. Overall Review Outcomes Mortality Morbidity Measuring Utilities Visual Analog Standard Gamble Time Trade-off Quality Adjusted Life Years (QALYs) QALYs = time (years) x quality (utils) Discounting NPV =  Utility / (1 + δ)t