Cost-Effectiveness Analysis and the Value of Research

David Meltzer MD, PhDThe University of Chicago

Overview

• Cost-effectiveness analysis has long been used to assess the value of medical treatments and the information that comes from diagnostic tests

• Newer value of information techniques have extended these tools to assess the value of medical research

• Understanding behaviors determining use of medical interventions in the context of heterogeneity is key to assessing their value and priorities for research

• Research may be especially valuable when it can be used to individualize care

Value of Medical Treatments

• Health effects– Length/quality of life: QALYs

• Cost effects• Choose all interventions for which

cost/QALY < threshold– Often $50-100K/QALY

• Widely accepted, >> 1000 applications

Value of Diagnostic Testing

Test

Don’t Test

S

H

S

H

Max{pU(T|S)+(1-p)U(T|H), pU(N|S)+(1-p)U(N|H)}

U(T|S)

U(N|H)

pU(T|S)+(1-p)U(N|H)

Cost-Effectiveness of Medical Interventions

Intervention Cost/LY

Neonatal PKU screening <0

Sec. prev. hyperchol. men age 55-64 2,000

Sec. prev. hyperchol. men age 75-84 25,000

Pri. prev. mild hyperchol. men age 55-64 99,000

Screening exercise test men age 40 124,000

Screening ultrasound every 5 yr. for AAA 907,000

Frequency

Increase inLE vs.

no screening

Increase inCost vs.

no screening

AverageCost perLife-YrSaved

MarginalIncrease

in LE

MarginalIncreasein Cost

MarginalCost per

Life-Yr Saved

3 years 70 days $500 $2,600/LY 70 days $500 $2,600/LY

2 years 71 days $750 $3,900/LY 1 day $250 $91,000/LY

1 year 71 days8 hours

$1,500 $7,300/LY 8 hours $750 $830,000/LY

Cost-Effectiveness of Pap Smears

Testing as Value of Information

Test

Don’t Test

S

H

S

H

Max{pU(T|S)+(1-p)U(T|H), pU(N|S)+(1-p)U(N|H)}

U(T|S)

U(N|H)

pU(T|S)+(1-p)U(N|H)

Research as Value of Information

Test

Don’t Test

S

H

S

H

Max{pU(T|S)+(1-p)U(T|H), pU(N|S)+(1-p)U(N|H)}

U(T|S)

U(N|H)

pU(T|S)+(1-p)U(N|H)

Value of Information Approach to Value of Research

• Without information– Make best compromise choice not knowing true state of the

world (e.g. don’t know if intervention is good, bad)• With probability p: get V(Compromise|G)• With probability 1-p: get V(Compromise|B)

• With information– Make best decision knowing true state

• With probability p: get V(Best choice|G)• With probability 1-p: get V(Best choice|B)

• Value of information = E(outcome) with information - E(outcome) w/o information

= {p*V(Best choice|G) + (1-p)*V(Best choice|B)} - {p*V(Compromise|G) + (1-p)*V(Compromise|B)}

= Value of Research

Practical Applications of Value of Information• Several full applications

– UK (NICE): Alzheimer’s Disease Tx, wisdom teeth removal– US (AHRQ): Hospitalist research– But needed data can be hard to obtain

• Bound with more limited data– Murphy/Topel: LE 3mo/yr*$50K/LY = $10K/person/yr = $3 Trillion/yr– Real value of research may be far less than expected, e.g., for prostate

cancer:• Maximal value of research = $ 5

Trillion• Expected value of perfect information = $21 Billion• Expected value of information = $ 1

Billion• Area of active investigation

– Most promising clearly for applied research

“Bayesian Value of information analysis: An application to a policy model of Alzheimer's disease.”

Uncertainty in Incremental Net Benefits

Cost-Effectiveness Acceptability Curve

Value of Research by Time Horizon

Value of Research by Value of Health

Contributors to Value of Research

Practical Applications of Value of Information• Several full applications

– UK (NICE): Alzheimer’s Disease Tx, wisdom teeth removal– US (AHRQ): Hospitalist research– But needed data can be hard to obtain

• Bound with more limited data– Murphy/Topel: LE 3mo/yr*$50K/LY = $10K/person/yr = $3 Trillion/yr– Real value of research may be far less than expected, e.g., for prostate cancer:

• Maximal value of research =$ 5 Trillion• Expected value of perfect information = $21 Billion• Expected value of information =$ 1 Billion

• Area of active investigation– Most promising clearly for applied research– Increasing interest among pharma

Behavioral Cost-Effectiveness Analysis

• Value of health interventions depend on how they are used– Especially in the presence of heterogeneity– True for treatments and for diagnostics

• Understanding behaviors determining use of health interventions key to their evaluation– Optimizing behavior: self-selection/diagnostic testing– Non-optimal behavior: non-selective use

Standard CEA with Heterogeneous Individuals

costs

effectiveness

m

CE

Blue Dots = Treated Patients

Optimal Selection with Heterogeneity: via Self-selection or Diagnostic Testing

costs

effectiveness

m

CE

Blue Dots=Pts gain from Tx; Orange Dots=Pts lose from Tx

Effect of Perfect Selection on CEA

costs

effectiveness

m

CE

m’

Blue Dots=Pts gain from Tx; Orange Dots=Pts lose from Tx (reject)

Empirical Selection

costs

effectiveness

m

CE

Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

Background: Diabetes in the Elderly • Diabetes care guidelines call for intensive lowering of

glucose among younger patients • However, unclear if this should apply to older patients

– Gains in life expectancy smaller– Side effects of treatment may dominate– CE models of intensive therapy in older patients:

• Minimal or even negative effects on QALYs• Not cost-effective

– Know many patients refuse intensive therapy• Suggests self-selection may have important effects on CEA

in diabetes

Methods

• Interviewed 500 older diabetes patients to obtain data on preferences – Conventional and intensive glucose lowering (using insulin or oral

medications) – Blindness, end-stage renal disease, lower extremity amputation

• Collected data on treatment choices and patient characteristics by medical records review

• Used CDC simulation model of intensive therapy for type 2 diabetes and patient-specific demographic, health, and preference data to get person-specific estimates of lifetime costs and benefits

• Analyses of cost-effectiveness of intensive vs. conventional therapy contrasting all patients vs. perfect self-selection vs. empirical self-selection

Results: Intensive vs. Conventional Therapy

CE Approach Group N Change in Costs ($)

Change in QALYs

CE Ratio ($/QALY)

Standard Full Population 543 8076 -0.49 --

Perfect Self-Selection Effect for Intensive Therapy

-5000

0

5000

10000

15000

20000

-8 -6 -4 -2 0 2 4

m m’

CE

Blue dots--the cost-effectiveness values of individuals with an expected benefit from intensive therapy. Orange dots-- the cost-effectiveness values of individuals with a decrement in expected benefits with intensive therapy.M-- CE ratio for whole population. M’—CE ratio after self-selection.

Results: Intensive vs. Conventional Therapy

CE Approach Group N Change in Costs ($)

Change in QALYs

CE Ratio ($/QALY)

Standard Full Population 543 8076 -0.49 --

Perfect Self-Selection QALY>0 403 8165 0.40 20K

QALY<0 131 7906 -3.25 --

Empirical Self-Selection Effect for Intensive Therapy

-5000

0

5000

10000

15000

20000

-8 -6 -4 -2 0 2 4

Blue dots-- cost-effectiveness values for individuals who identify their care as intensive therapy.Orange dots-- cost-effectiveness values for all other individuals. M-- CE ratio for orange dot individuals. M’-- CE ratio for blue dot individuals.

Results: Intensive vs. Conventional Therapy

CE Approach Group N Change in Costs ($)

Change in QALYs

CE Ratio ($/QALY)

Standard Full Population 543 8076 -0.49 --

Perfect Self-Selection QALY>0 403 8165 0.40 20K

QALY<0 131 7906 -3.25 --

Empirical Self-Selection

Self-identified intensive therapy

154 7948 0.17 47K

All others 364 8164 -0.80 --

Implications - I

• Results of standard CEA may be misleading– In contrast to the suggestion of standard CEA, offering

intensive glucose lowering to all older people likely cost-effective

– CEAs should consider the importance of self-selection

• Distinction between perfect and empirical self-selection is potentially important– Data on who will use a treatment if it is offered is

important

Implications - II

• A framework to account for heterogeneity in patient benefits is key to valuing diagnostic tests, guidelines, decision-aids, or improved patient-doctor communication that can make care more consistent with variation in patient benefits

Motivation for Diagnostic Test/Decision Aids

costs

effectiveness

m

CE

Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

Aim of Diagnostic Test/Decision Aids

costs

effectiveness

m

CE

Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

Value of Diagnostic Test/Decision Aids

costs

effectiveness

m

CE

Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

c

e

Value of Diagnostic Test/Decision Aid

• Effectiveness = Pts e

• Costs = Pts c• Total Benefit

Cost-Benefit = (1/ Pts e + Pts

c

Net Health Benefit = Pts e + Pts

c

Per Capita Value of Identifying Best Population-level and Individual-level

Treatment in Prostate Cancer

Value

Best Population-level Treatment $29

Best Individual-level Treatment $2958

Implications - III

• Modeling heterogeneity and selection suggests a framework to design co-payment systems to enhance the cost-effectiveness of therapies

Motivation for Copayment (c)

costs

effectiveness

m

CE

Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

c

Motivation for Copayment (c)

costs

effectiveness

m

CE

Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

c

Per Capita Value of Identifying Best Population-level and Individual-level Care

in Prostate Cancer with Full Insurance

Value

Best Population-level Therapy $29

Best Individual-level Therapy $2958

Best Individual-level Therapy with Full Insurance $41

Conclusions• Cost-effectiveness analysis can be used to value

diagnostic testing and research on diagnostic testing– Approaches exist to bound calculations with limited data

• Understanding behaviors determining use of medical interventions in the context of heterogeneity is key to assessing their value and priorities for research– Research may be especially valuable when it can be used

to individualize care– Insurance and other determinants of use can significantly

alter value of research

Implications of Empirical CEA

• Need to consider how a treatment will be used in deciding if it will be welfare improving

• Highlights importance of efforts to promote selective use of treatments– Biomarkers valuable if encourage selective use of

treatments• Need to consider how a biomarker will be used in

deciding if it will be welfare improving• Highlights importance of efforts to promote selective use

of biomarkers– Biomarkers valuable if encourage selective use of

treatments

Non-selective Use and Empirical Cost-effectiveness

• Cost-effectiveness analyses of interventions often stratify cost-effectiveness by indication

• Yet technologies are often used non-selectively• The actual (empirical) costs and effectiveness of

an intervention may be strongly influenced by patterns of use

Example: Cox-2 Inhibitors vs. NSAIDs

QALY COST ($) $/QALY Fraction Users

High Risk 0.085 4,721 56K 39%

Low Risk 0.026 14,123 537K 61%

Overall 0.042 11,584 276K