2014 AMCP P&T Competition Competition Tips and P harmacoeconomic Basics

1. 2014 AMCP P&T CompetitionCompetition Tips and Pharmacoeconomic Basics David E. Matthews, PharmD 2012 P&T National Finalist OSU Academy of Managed Care Pharmacy November 25, 2013

2. Presentation Outline • History of the chapter in the competition • Tips for the competition • Introduction to pharmacoeconomics

3. P&T Competition: OSU AMCP Chapter • Many appearances at nationals, especially mid ’00s • Highest finish 2nd place nationally: • 2007: Amanda Bain, Jessica Dell’Omo, Laura Koop, Philip Schwieterman • 2008: Laura Koop, EleniLekas, NeginSoufi-Siavash, Dennis Sperle • Last appearance at nationals was 2012

4. 2007 OSU Team2ndPlace Nationally • Laura Koop (P1), Jessica Dell’Omo (P3), Amanda Bain (P4), Philip Schwieterman (P3)

5. 2014 AMCP P&T Competition: Eylea® • Project components • Questions A-D • Drug monograph • Presentation

6. Questions A-D • Recommendation: start on this first • Will help later when it comes time to start on the monograph • Brainstorm ideas together, but assign individual responsibility • Proofread each other’s work

7. Drug Monograph • Most time consuming element • Start early and aim to finish early • Allow time for plenty of proofreading • Divide responsibility but also collaborate • Look at sample monographs if available • Set aside plenty of time to meet as a team in the days prior to the due date • Google docs • Beware of formatting issues

8. Presentation • Finish monograph and written responses first • Will have ~1 week between monograph submission and due date for slides • Set aside plenty of time to meet as a team in the days prior to the due date • Rehearse many times before presenting • Anticipate possible questions and practice your response

9. How to divide up the work? • Clinical expert? • Economic expert? • Submission format expert? • Each teammate should have a basic understanding of your entire group’s work!

10. 2012 P&T Team – National Finalists Dave, P3 Vanessa, P1 Becky, P3 Anne, P2 AMCP format for dossier submission Clinical trial evidence Pharmacokinetics, drug interactions, monitoring Pharmacoeconomic evidence and modeling

11. 2013 P&T Team – Local Chapter Champions Carolyn, P2 Dave, P4 Taylor, P1 Lisa, P3 Clinical trial evidence Pharmacokinetics, drug interactions Pharmacoeconomic evidence and modeling AMCP format for dossier submission

12. Pharmacoeconomic Basics

13. What is Pharmacoeconomics? • Economics is the science of balancing best outcomes with limited resources • Pharmacoeconomics applies this concept to pharmacologic interventions

14. Types of Economic Analyses • Cost-minimization analysis • Cost-benefit analysis • Cost-effectiveness analysis • Cost-utility analysis

15. Cost-Minimization Analysis • Compares two interventions considered equally effective and tolerable • Determines which intervention costs less • Costs can include more than the price of medication • E.g. drug monitoring or other healthcare services

16. Cost-Benefit Analysis • Adds up costs associated with intervention • Compares to monetary benefits of intervention • Outcomes must be converted to dollars • Compares input dollars vs. output dollars • Determines whether benefits > cost

17. Cost-Effectiveness Analysis • Determines the cost to produce an effect • Expresses cost of an effect as a ratio: • Numerator = cost ($) • Denominator = clinically appropriate marker, for example: • mm Hg blood pressure lowering • mg/dL of LDL lowering • Quality-adjusted life-years (cost-utility analysis: see next slide)

18. Cost-Utility Analysis • Subset of cost-effectiveness analysis • Determines the cost of adding one year of perfect health to a patient’s life • Calculates incremental cost-effectiveness ratio (ICER) • Ratio of cost to effectiveness: • Numerator = cost ($) • Denominator = Quality-adjusted life-years

19. Cost-Saving ≠ Cost-Effective! • Cost-saving • An intervention that has a lower total cost than an alternative intervention • Cost-effective • An intervention that is sufficiently effective relative to its total cost when compared with an alternative intervention

20. Domination • Occurs when one treatment is cheaper AND more effective • The cheaper/more effective treatment “dominates” the alternative and is the preferred treatment

21. Cost-Effectiveness Plane PERFORM CEA DOMINATED cost NW quadrant: more costly, less effective NE quadrant: more costly, more effective effect effect SW quadrant: less costly, less effective SE quadrant: less costly, more effective PERFORM CEA DOMINATES cost Adapted from: Smith KJ et al. In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 95-108.

22. Determining Cost-Effectiveness • New intervention in NE or SW quadrant • Example: • Drug A is a new drug • Drug B is the current standard of care • Drug A works better than Drug B • Drug A is more costly than Drug B • Question: • Using Drug A instead of Drug B, how much does it cost us to add one year of perfect health onto the life of our patient?

23. Incremental Cost-Effectiveness Ratio (ICER) Represents the amount of money spent to add one year of perfect health onto the life of our patient

24. KEY POINT: The ICER is the single most important indicator of an intervention’s cost-effectiveness. Its calculation can be complex, and will be the focus of the next several slides.

25. Terminology • Utility • Numerical estimate of quality of life (QOL) associated with a disease state or treatment • Perfect health = 1, Dead = 0 • Anything else…somewhere in between • Measured using questionnaires

26. Terminology • Quality-Adjusted Life-Year (QALY) • Life expectancy adjusted based on utility • QALY = time in health state × utility of state

27. QALY Example • Consider 2 hypothetical chemo drugs • Standard of care vs. new therapy • Both prolong life • Both cause side effects which reduce QOL

28. QALY Example • Standard of care treatment: • Prolongs life by an average of 1 year • Estimated utility of 0.65 due to side effects • New treatment: • Prolongs life by an average of 1.5 years • Estimated utility of 0.5 due to side effects

29. Standard of Care QALYs QALY = Life expectancy × utility = 1 year × 0.65 utility = 0.65 QALYs The standard of care is expected to add 0.65 quality-adjusted life-years to our patient’s life.

30. New Treatment QALYs QALY = Life expectancy × utility = 1.5 years × 0.5 utility = 0.75 QALYs The new treatment is expected to add 0.75 quality-adjusted life-years to our patient’s life.

31. Calculating ICER ICER = difference in cost difference in effectiveness Or… ICER = C2 – C1$’s E2 – E1 QALYs

32. Back to Our Chemo Drugs… • Suppose a full course of treatment costs… • $12,000 for standard of care • $15,000 for new treatment

33. ICER of Chemo Drugs ICER = C2 – C1 E2 – E1 ICER = $15,000 – $12,000 0.75 QALY – 0.65 QALY ICER = $30,000/QALY

34. Interpretation of ICER On average, it costs us $30,000 to add one year of perfect health onto the life of our patient. So is this considered cost-effective?

35. Threshold of Cost-Effectiveness • Subjective • $50,000/QALY commonly reported in studies • WHO recommends 3x per capita GDP for a given country • Would be around $150,000/QALY in USA • National Institute for Health and Clinical Experience (NICE) recommends £30,000/QALY ($48,396/QALY) Dasbach EJ et al.. In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 119-143. World Health Organization. Available from: http://www.who.int/choice/costs/CER_thresholds/en/index.html McCabe C et al.. Pharmacoeconomics. 2008;26(9):733-44. Review.

36. Problems with Oversimplification • Much more complex than “averages” in the real world • Some people will tolerate the drugs better or worse than others • Patients do not remain in one health state • Each individual experiences different quality of life, incurs different costs, etc.

37. Markov Models • Common in pharmacoeconomic research • Used to calculate the entire cost and QALYs gained for a population • Uses a hypothetical cohort of patients • Patients move between health states • Each state has associated probabilities, costs, and utilities

38. Components of Markov Models • Expected health states • Probabilities related to treatment failure, side effects, etc. • Normally from probabilities seen in studies • Cycle length • How frequently would patients be expected to transition through health states? • Utility and cost estimates for each state • Time horizon

39. Example • New treatment for a terminal illness • More costly, more effective than standard of care • Patients whose disease progresses incur greater costs • Hospitalizations • More treatments

40. Summary of Therapies to be Analyzed

41. Example Markov Model • Cycles patients through health states based on preset probabilities • Example model: • Healthy • Sick • Dead • Each state is assigned its own utility and cost

42. Markov Model Framework Healthy Sick Dead

43. Markov Model FrameworkStandard of Care Healthy Sick Dead

44. Markov Model FrameworkNew Treatment Healthy Sick Dead

45. Health State Utilities • Healthy • Utility = 0.8 (not 1.0 due to side effects) • Sick • Utility = 0.4 • Dead • Utility = 0

46. 10,000 Patient Cohort:New Treatment Healthy 10,000 pts Sick Dead

47. After 1 month Healthy COST: 9,600 x $1,500 =$14.4M QALY: 1/12 x 9,600 x 0.8 =640 QALY 9,600 pts COST: 400 x $3,200 =$1.3M QALY: 1/12 x 400 x 0.4 =13 QALY Sick 400 pts Dead

48. After 2 months Healthy 9,216 pts COST: 9,216 x $1,500 =$13.8M QALY: 1/12 x 9,216 x 0.8 =614 QALY COST: 744 x $3,200 =$2.4M QALY: 1/12 x 744 x 0.4 =25 QALY Sick 744 pts Dead 40 pts

49. After 3 months Healthy 8,847 pts COST: 8,847 x $1,500 =$13.2M QALY: 1/12 x 8,847 x 0.8 =590 QALY COST: 1,039 x $3,200 =$3.3M QALY: 1/12 x 1,039 x 0.4 =35 QALY Sick 1,039 pts Dead 114 pts And so on until all patients are in the “absorbing state” (death)

50. Markov Model Results • Model continues until all patients in absorbing state or time horizon complete • Patients accrue QALYs and costs each cycle • Separate models run for new treatment and standard of care • Once complete, ICER is calculated • (difference in cost) / (difference in QALYs)