Loading in 5 sec....

Outcomes in Decision Analysis: Utilities, QALYs, and DiscountingPowerPoint Presentation

Outcomes in Decision Analysis: Utilities, QALYs, and Discounting

- 299 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about 'Outcomes in Decision Analysis: Utilities, QALYs, and Discounting' - kenley

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### Outcomes in Decision Analysis: Utilities, QALYs, and Discounting

### Overview Discounting

### Review—Last Lecture Discounting

### Preview—Where We Are Going with this Analysis? Discounting

### What is a Utility? Discounting

### Utilities Discounting

### How are utilities measured? Discounting

### BKA vs. AKA Example Discounting

### Standard Gamble Discounting

### Time Tradeoff Discounting

### Pros and Cons - VAS Discounting

### Pros and Cons – SG Discounting

### Pros and Cons – TTO Discounting

### Utilities in decision analysis Discounting

### QALYs Discounting

### Outcomes - Discounting Discounting

### Outcomes - Discounting Discounting

### Outcomes - Discounting Discounting

### Outcomes - Discounting Discounting

### Discounting – Special Topic Discounting

### Discounting – Special Topic Discounting

### Exponential vs. PBP Discounting

### Overall Review Discounting

Aaron B. Caughey, MD, PhD

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

Disclosures Discounting

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

- Hellman Foundation

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

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

To Clip or Not To Clip Discounting

=.9825

=.9921

=.55

=1.0

=.55

=.977

Diff = -0.0151

=0

.865 vs .977

To Clip or not to Clip? Discounting

- 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

Quantifying Health Outcomes Discounting

- 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

Health Outcomes – Mortality Discounting

- 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

Health Outcomes – Morbidity Discounting

- 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

To Clip or not to Clip? Discounting

- 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

How do we incorporate quality-of-life effects into DA? Discounting

- Measure/estimate and apply utilities
- Use utilities to quality-adjust life expectancy for decision and cost-effectiveness 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

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)

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

Utilities are commonly estimated using comparisons to the 0 and 1 anchors

Visual Analog Scale

Standard Gamble

Time Trade-off

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

For which outcomes do we need to measure utilities? Discounting

- 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?

Visual Analog Scaling Discounting

Full health: intact leg

100

98

99

65

BKA

55

AKA

2

1

Dead

0

Outcomes rated on a 0-to-100 “feeling thermometer.”

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)

Standard Gamble E Discountingxercise

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

Standard Gamble Discounting

- 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

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)

Time Tradeoff Preference Elicitation Discounting

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)

Time Tradeoff Preference Elicitation Discounting

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)

Utility Measurement – Time Trade-off Discounting

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

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

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

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.”)

Utilities can adjust life expectancy in DA where outcomes include morbidity/quality-of-life effects.

Quality Adjusted Life-Years (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

Back to aneurysm Discounting

Including utility for early death and stroke=0.5 Discounting

Adding utility for worry =.95 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

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

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

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}

QALYs Discounting

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

Exponential Discounting 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

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?

Exponential Discounting 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

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

Present-Biased Preferences Discounting

- 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

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

Discounting: DiscountingPrescriptive vs. Descriptive

- We discount
- But, should we
- Example - perceived time

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

Download Presentation

Connecting to Server..