Cost-Effectiveness Analysis and the Value of Research by 3g92726

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									Cost-Effectiveness Analysis
 and the Value of Research


    David Meltzer MD, PhD
   The 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
  Dcost/DQALY < threshold
  – Often $50-100K/QALY
• Widely accepted, >> 1000 applications
 Value of Diagnostic Testing

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

                      U(N|H)
             H



             S
Don’t Test           Max{pU(T|S)+(1-p)U(T|H),
                         pU(N|S)+(1-p)U(N|H)}
                 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
    Cost-Effectiveness of Pap Smears
                                          Average
             Increase in    Increase in   Cost per    Marginal   Marginal     Marginal
               LE vs.         Cost vs.    Life-Yr     Increase   Increase      Cost per
Frequency   no screening   no screening    Saved       in LE      in Cost   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 days        $1,500       $7,300/LY    8 hours    $750      $830,000/LY
              8 hours
Testing as Value of Information

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

                         U(N|H)
                H



                S
   Don’t Test           Max{pU(T|S)+(1-p)U(T|H),
                            pU(N|S)+(1-p)U(N|H)}
                    H
Research as Value of Information

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

                          U(N|H)
                 H



                 S
    Don’t Test           Max{pU(T|S)+(1-p)U(T|H),
                             pU(N|S)+(1-p)U(N|H)}
                     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: DLE 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: DLE 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

                                              CE
                       D costs


                                 m



                                     D effectiveness




            Blue Dots = Treated Patients
Optimal Selection with Heterogeneity:
via Self-selection or Diagnostic Testing
                                               CE
                        D costs


                                  m



                                      D effectiveness




Blue Dots=Pts gain from Tx; Orange Dots=Pts lose from Tx
      Effect of Perfect Selection on CEA

                                                      CE
                             D costs


                                       m      m’



                                           D effectiveness




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

                                            CE
                     D costs


                               m



                                   D effectiveness




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 Change in CE Ratio
                                      Costs ($)  QALYs    ($/QALY)

Standard      Full Population   543     8076      -0.49      --
             Perfect Self-Selection Effect for Intensive Therapy
                                                                20000



                                                                15000                 CE


                                                                10000



                                                                  5000
                                                            m                                    m’
                                                                        0
        -8               -6              -4               -2                0               2                4


                                                                 -5000
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 Change in CE Ratio
                                        Costs ($)  QALYs    ($/QALY)

Standard        Full Population   543     8076      -0.49      --

Perfect Self-
                DQALY>0           403     8165      0.40      20K
Selection
                DQALY<0           131     7906      -3.25      --
       Empirical Self-Selection Effect for Intensive Therapy

                                                           20000


                                                           15000


                                                           10000


                                                             5000


                                                                   0
     -8              -6              -4               -2               0               2              4

                                                           -5000

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 Change in CE Ratio
                                         Costs ($)  QALYs    ($/QALY)

Standard         Full Population   543     8076      -0.49      --

Perfect Self-
                 DQALY>0           403     8165      0.40      20K
Selection
                 DQALY<0           131     7906      -3.25      --

                 Self-identified
Empirical
                 intensive         154     7948      0.17      47K
Self-Selection
                 therapy
                 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

                                               CE
                        D costs


                                  m



                                      D effectiveness




   Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
Aim of Diagnostic Test/Decision Aids

                                            CE
                     D costs


                               m



                                   D effectiveness




Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
Value of Diagnostic Test/Decision Aids

                                             CE
                     D costs


                               m



                               Dc   D effectiveness
                          De




Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
 Value of Diagnostic Test/Decision Aid

• Effectiveness = Pts D De
• Costs = Pts D Dc
• Total Benefit
   Cost-Benefit =      (1/l) Pts D De +   Pts D Dc
   Net Health Benefit =      Pts D De + l Pts D Dc
   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 (pc)

                               pc            CE
                     D costs


                               m



                                    D effectiveness




Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
    Motivation for Copayment (pc)

                               pc            CE
                     D costs


                               m



                                    D effectiveness




Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx
   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
                                 $41
with Full Insurance
                     Conclusions
• Cost-effectiveness analysis can be used to value
  diagnostic testing and research on diagnostic testing
   – Approaches exist to bound calculations with limited data
• Understanding behaviors determining use of
  medical interventions in the context of
  heterogeneity is key to assessing their value and
  priorities for research
   – Research may be especially valuable when it can be used
     to individualize care
   – Insurance and other determinants of use can significantly
     alter value of research
       Implications of Empirical CEA
• Need to consider how a treatment will be used in deciding
  if it will be welfare improving
• Highlights importance of efforts to promote selective use
  of treatments
   – Biomarkers valuable if encourage selective use of
       treatments
• Need to consider how a biomarker will be used in deciding
  if it will be welfare improving
• Highlights importance of efforts to promote selective use
  of biomarkers
   – Biomarkers valuable if encourage selective use of
       treatments
   Non-selective Use and Empirical
         Cost-effectiveness
• Cost-effectiveness analyses of interventions often
  stratify cost-effectiveness by indication
• Yet technologies are often used non-selectively
• The actual (empirical) costs and effectiveness of
  an intervention may be strongly influenced by
  patterns of use
Example: Cox-2 Inhibitors vs. NSAIDs
            DQALY   DCOST ($)   $/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

								
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