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

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### 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

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 ’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

2007 OSU Team2ndPlace Nationally

- Laura Koop (P1), Jessica Dell’Omo (P3), Amanda Bain (P4), Philip Schwieterman (P3)

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, P3

Vanessa, P1

Becky, P3

Anne, P2

AMCP format for dossier submission

Clinical trial evidence

Pharmacokinetics,

drug interactions, monitoring

Pharmacoeconomic evidence and modeling

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

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/dL 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

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.

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

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 quality-adjusted 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 quality-adjusted life-years to our patient’s life.

Calculating ICER

ICER = difference in cost

difference in effectiveness

Or…

ICER = C2 – C1$’s

E2 – E1 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 = C2 – C1

E2 – E1

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 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.

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

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

Health State Utilities

- Healthy
- Utility = 0.8 (not 1.0 due to side effects)
- Sick
- Utility = 0.4
- Dead
- Utility = 0

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

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

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)

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 cost-effectiveness
- Markov models are used to calculate ICER

References

- McGhan 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; c2012 [cited 7 Oct 2012]. Available from: http://www.who.int/choice/costs/CER_thresholds/en/index.html
- McCabe 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.

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