"Cost-Effectiveness Analysis and the Value of Research" (PPT).
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Transcript of "Cost-Effectiveness Analysis and the Value of Research" (PPT).
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Cost-Effectiveness Analysis and the Value of Research
David Meltzer MD, PhDThe University of Chicago
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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
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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
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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)
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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
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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
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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)
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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)
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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
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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
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“Bayesian Value of information analysis: An application to a policy model of Alzheimer's disease.”
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Uncertainty in Incremental Net Benefits
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Cost-Effectiveness Acceptability Curve
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Value of Research by Time Horizon
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Value of Research by Value of Health
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Contributors to Value of Research
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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
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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
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Standard CEA with Heterogeneous Individuals
costs
effectiveness
m
CE
Blue Dots = Treated Patients
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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
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Effect of Perfect Selection on CEA
costs
effectiveness
m
CE
m’
Blue Dots=Pts gain from Tx; Orange Dots=Pts lose from Tx (reject)
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Empirical Selection
costs
effectiveness
m
CE
Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
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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
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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
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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 --
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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.
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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 --
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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.
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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 --
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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
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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
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Motivation for Diagnostic Test/Decision Aids
costs
effectiveness
m
CE
Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
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Aim of Diagnostic Test/Decision Aids
costs
effectiveness
m
CE
Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
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Value of Diagnostic Test/Decision Aids
costs
effectiveness
m
CE
Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
c
e
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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
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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
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Implications - III
• Modeling heterogeneity and selection suggests a framework to design co-payment systems to enhance the cost-effectiveness of therapies
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Motivation for Copayment (c)
costs
effectiveness
m
CE
Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
c
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Motivation for Copayment (c)
costs
effectiveness
m
CE
Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
c
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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
$41
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Conclusions
• Cost-effectiveness analysis can be used to value diagnostic testing and research on diagnostic testing– Approaches exist to bound calculations with limited data
• Understanding behaviors determining use of medical interventions in the context of heterogeneity is key to assessing their value and priorities for research– Research may be especially valuable when it can be used
to individualize care– Insurance and other determinants of use can significantly
alter value of research
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Implications of Empirical CEA
• Need to consider how a treatment will be used in deciding if it will be welfare improving
• Highlights importance of efforts to promote selective use of treatments– Biomarkers valuable if encourage selective use of
treatments• Need to consider how a biomarker will be used in
deciding if it will be welfare improving• Highlights importance of efforts to promote selective use
of biomarkers– Biomarkers valuable if encourage selective use of
treatments
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Non-selective Use and Empirical Cost-effectiveness
• Cost-effectiveness analyses of interventions often stratify cost-effectiveness by indication
• Yet technologies are often used non-selectively• The actual (empirical) costs and effectiveness of
an intervention may be strongly influenced by patterns of use
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Example: Cox-2 Inhibitors vs. NSAIDs
QALY COST ($) $/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