Competition 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 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 (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
Pharmacokinetics, drug interactions
Pharmacoeconomic evidence and modeling
AMCP format for dossier submission
Clinical trial evidence

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 cost DOMINATED
NW quadrant: more costly, less effective effect
NE quadrant: more costly, more effective
PERFORM
CEA effect
SW quadrant: less costly, less effective
SE quadrant: less costly, more effective
DOMINATES PERFORM
CEA 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

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
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.

 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

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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
Progression from healthy to sick per month
Cost of tx + disease progression per month
Progression from sick to death per month
$800
8%
$2,500
20%
$1,500
4%
$3,200
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

Healthy
Sick
Dead

Healthy
0.08
Sick
0.20
Dead
0.92
0.80
Therapy Standard of care
8% Progression from healthy to sick per month
Progression from sick to death per month
20%

Healthy
0.04
Sick
0.10
Dead
0.96
0.90
Therapy New treatment
4% Progression from healthy to sick per month
Progression from sick to death per month
10%

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

Healthy
10,000 pts
0.04
Sick
0.1
Dead
0.96
0.9

After 1 month
Healthy
Sick
9,600 pts
400 pts
0.04
0.1
Dead
0.96
0.9
COST: 9,600 x $1,500
=$14.4M
QALY: 1/12 x 9,600 x 0.8
=640 QALY
COST: 400 x $3,200
=$1.3M
QALY: 1/12 x 400 x 0.4
=13 QALY

After 2 months
Healthy
Sick
Dead
9,216 pts
744 pts
0.04
0.1
40 pts
0.96
0.9
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

After 3 months
Healthy
Sick
Dead
8,847 pts
0.04
1,039 pts
0.1
114 pts
0.96
0.9
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
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
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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.