1. Cost-Effectiveness Analysis and the Value of Research David Meltzer MD, PhD The University of Chicago

2. 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

3. 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

4. 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)

5. Cost-Effectiveness of Medical Interventions

6. Frequency Increase in LE vs. no screening Increase in Cost vs. no screening Average Cost per Life-Yr Saved Marginal Increase in LE Marginal Increase in Cost Marginal Cost per Life-Yr Saved 3 years70 days$500$2,600/LY70 days$500$2,600/LY 2 years71 days$750$3,900/LY1 day$250$91,000/LY 1 year71 days 8 hours $1,500$7,300/LY8 hours$750$830,000/LY Cost-Effectiveness of Pap Smears

7. 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)

8. 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)

9. 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

10. 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

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

12. Uncertainty in Incremental Net Benefits

13. Cost-Effectiveness Acceptability Curve

14. Value of Research by Time Horizon

15. Value of Research by Value of Health

16. Contributors to Value of Research

17. 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

18. 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

19. Standard CEA with Heterogeneous Individuals  costs  effectiveness m CE Blue Dots = Treated Patients

20. 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

21. Effect of Perfect Selection on CEA  costs  effectiveness m CE m’ Blue Dots=Pts gain from Tx; Orange Dots=Pts lose from Tx (reject)

22. Empirical Selection  costs  effectiveness m CE Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

23. 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

24. 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

25. Results: Intensive vs. Conventional Therapy CE ApproachGroupNChange in Costs ($) Change in QALYs CE Ratio ($/QALY) StandardFull Population5438076-0.49--

26. Perfect Self-Selection Effect for Intensive Therapy 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.

27. Results: Intensive vs. Conventional Therapy CE ApproachGroupNChange in Costs ($) Change in QALYs CE Ratio ($/QALY) StandardFull Population5438076-0.49-- Perfect Self- Selection  QALY>0 40381650.4020K  QALY<0 1317906-3.25--

28. Empirical Self-Selection Effect for Intensive Therapy 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.

29. Results: Intensive vs. Conventional Therapy CE ApproachGroupNChange in Costs ($) Change in QALYs CE Ratio ($/QALY) StandardFull Population5438076-0.49-- Perfect Self- Selection  QALY>0 40381650.4020K  QALY<0 1317906-3.25-- Empirical Self-Selection Self-identified intensive therapy 15479480.1747K All others3648164-0.80--

30. 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

31. 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

32. Motivation for Diagnostic Test/Decision Aids  costs  effectiveness m CE Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

33. Aim of Diagnostic Test/Decision Aids  costs  effectiveness m CE Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx

34. Value of Diagnostic Test/Decision Aids  costs  effectiveness m CE Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx cc ee

35. 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

36. 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

37. Implications - III Modeling heterogeneity and selection suggests a framework to design co-payment systems to enhance the cost-effectiveness of therapies

38. Motivation for Copayment (  c)  costs  effectiveness m CE Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx cc

39. Motivation for Copayment (  c)  costs  effectiveness m CE Blue Dots=Pts choose Tx; Orange Dots=Pts reject Tx cc

40. 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

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

43. 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

44. 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

45. Example: Cox-2 Inhibitors vs. NSAIDs  QALY  COST ($) $/QALYFraction Users High Risk0.0854,72156K39% Low Risk0.02614,123537K61% Overall0.04211,584 276K