2014 AMCP PT Competition Tips and Pharmacoeconomic Basics

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

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

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

2007 OSU Team 2 nd Place Nationally Laura Koop (P 1), Jessica Dell’Omo (P 3), Amanda Bain (P 4), Philip Schwieterman (P 3)

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

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

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

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

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!

2012 P&T Team – National Finalists Dave, P 3 Vanessa, P 1 AMCP format for dossier submission Becky, P 3 Anne, P 2 Clinical trial evidence Pharmacokinetics, drug interactions, monitoring Pharmacoeconomic evidence and modeling

2013 P&T Team – Local Chapter Champions Carolyn, P 2 Dave, P 4 Taylor, P 1 Pharmacokinetics, Pharmacoeconomic AMCP format drug interactions evidence and for dossier modeling submission Lisa, P 3 Clinical trial evidence

Pharmacoeconomic Basics

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

Types of Economic Analyses Cost-minimization analysis Cost-benefit analysis Cost-effectiveness analysis Cost-utility analysis

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

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

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/d. L of LDL lowering Quality-adjusted life-years (cost-utility analysis: see next slide)

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

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

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

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

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?

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

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.

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

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

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

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

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 qualityadjusted life-years to our patient’s life.

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 qualityadjusted life-years to our patient’s life.

Calculating ICER = difference in cost difference in effectiveness Or… ICER = C 2 – C 1 $’s E 2 – E 1 QALYs

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

ICER of Chemo Drugs ICER = C 2 – C 1 E 2 – E 1 ICER = $15, 000 – $12, 000 0. 75 QALY – 0. 65 QALY ICER = $30, 000/QALY

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?

Threshold of Cost-Effectiveness Subjective $50, 000/QALY commonly reported in studies WHO recommends 3 x 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 Mc. Cabe C et al. . Pharmacoeconomics. 2008; 26(9): 733 -44. Review.

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.

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

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

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

Summary of Therapies to be Analyzed Therapy Standard of care New treatment Cost of treatment, one month $800 $1, 500 Progression from healthy to sick per month 8% 4% Cost of tx + disease progression per month $2, 500 $3, 200 Progression from sick to death per month 20% 10%

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

Markov Model Framework Healthy Sick Dead

Markov Model Framework Standard of Care Healthy 0. 92 0. 08 Sick 0. 80 0. 20 Dead Therapy Standard of care Progression from healthy to sick per month 8% Progression from sick to death per month 20%

Markov Model Framework New Treatment Healthy 0. 96 0. 04 Sick 0. 90 0. 10 Dead Therapy New treatment Progression from healthy to sick per month 4% Progression from sick to death per month 10%

Health State Utilities Healthy Sick Utility = 0. 8 (not 1. 0 due to side effects) Utility = 0. 4 Dead Utility = 0

10, 000 Patient Cohort: New Treatment Healthy 10, 000 pts 0. 96 0. 04 Sick 0. 9 0. 1 Dead

After 1 month Healthy 9, 600 pts 0. 96 0. 04 Sick 400 pts 0. 1 Dead 0. 9 COST: 9, 600 x $1, 500 =$14. 4 M QALY: 1/12 x 9, 600 x 0. 8 =640 QALY COST: 400 x $3, 200 =$1. 3 M QALY: 1/12 x 400 x 0. 4 =13 QALY

After 2 months Healthy 9, 216 pts 0. 96 0. 04 Sick 744 pts 0. 1 Dead 40 pts 0. 9 COST: 9, 216 x $1, 500 =$13. 8 M QALY: 1/12 x 9, 216 x 0. 8 =614 QALY COST: 744 x $3, 200 =$2. 4 M QALY: 1/12 x 744 x 0. 4 =25 QALY

After 3 months Healthy 8, 847 pts 0. 96 0. 04 Sick 1, 039 pts 0. 1 Dead 0. 9 COST: 8, 847 x $1, 500 =$13. 2 M QALY: 1/12 x 8, 847 x 0. 8 =590 QALY COST: 1, 039 x $3, 200 =$3. 3 M QALY: 1/12 x 1, 039 x 0. 4 =35 QALY 114 pts And so on until all patients are in the “absorbing state” (death)

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)

Markov Models in the Real World Theoretically, models could be completed by hand Real life models become much more complex More health states Ability to move more freely through states Account for issues such as adverse events Computers solve complex models

Real Life Example Shaheen NJ et al. Gut. 2004 Dec; 53(12): 1736 -44.

Problems with Markov Models Complex models are difficult to understand Validity of model depends upon utility and cost estimates Sensitivity analysis to account for variability

Sensitivity Analysis The scenario based off initial estimates is called the “base case scenario” Real life probabilities and costs may be higher or lower than predicted Adjust assumptions upward and downward and recalculate ICER Provides a range of possible economic outcomes

Conclusion New interventions are usually more effective but at a higher price Cost-effectiveness analysis helps determine if a new intervention is effective enough to be worth our limited resources ICER is a numerical value that summarizes costeffectiveness Markov models are used to calculate ICER

Questions?

References Mc. Ghan WF. Introduction to pharmacoeconomics. In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 1 -16. Haycox A. What is cost-minimization analysis? In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 83 -94. Smith KJ and Robers MS. Cost-effectiveness analysis. In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 95 -108. Dasbach EJ, Insinga RP, and Elbasha EH. Cost-utility analysis: a case study of a quadrivalent human papillomavirus vaccine. In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 119 -143. Beck JR. Markov modeling in decision analysis. In: Arnold, RJG, editor. Pharmacoeconomics from theory to practice. Boca Raton: CRC Press; 2010. p. 47 -58. World Health Organization. Choosing interventions that are cost-effective [Internet]. [Geneva]: WHO; c 2012 [cited 7 Oct 2012]. Available from: http: //www. who. int/choice/costs/CER_thresholds/en/index. html Mc. Cabe C, Claxton K, Culyer AJ. The NICE cost-effectiveness threshold: what it is and what that means. Pharmacoeconomics. 2008; 26(9): 733 -44. Review. Shaheen NJ, Inadomi JM, Overholt BF, Sharma P. What is the best management strategy for high grade dysplasia in Barrett's oesophagus? A cost effectiveness analysis. Gut. 2004 Dec; 53(12): 1736 -44.