1. Terminology and Jargon Demystified
Meg Franklin, PharmD, PhD
Franklin Pharmaceutical Consulting
March 27, 2015

2. 03 01 04 02 05 Intro table of contents
Health Econ Why are we here? 03
Decision Analysis The life of a tree Calculations 01
Study Design Types of studies Metrics to report 04
Economic Models Definitions Trends 02
Cost Analyses Usual suspects Trends 05
Conclusions Lessons learned Looking ahead

3. Joke of the Day
How many pharmacoeconomists does it take to change a light bulb?
Four
1 to estimate the cost of the new light bulb
1 to estimate the life expectancy of this new light bulb
1 to estimate the QOL associated with the light from the new light bulb
1 to package the information so that it convinces the healthcare decision- maker to take out the old light bulb and put in a new one.

4. Why are we here? Introduction Improve writing Communicate effectively
Share ideas and experiences
Develop strategies for publications
Focus on:
Nomenclature
What to report
Common pitfalls
Calvin and Hobbes, by Bill Watterson
Introduction

5. Study Design
Types of Studies
Metrics to Report

6. Observational study designs
Cross-sectional
Case-Control
Cohort
Today
Historical Control
Study Design
Adapted from Figure 2-5 in Basic & Clinical Biostatistics (4th Ed).

7. Observational Studies
Study Design
Observational study designs
Observational Studies
Study Design
Definition
Common uses
Case series
Reports characteristics of a small group
Hypothesis generation
Cross-sectional
Reports data on a group of subjects at one time rather than over a period of time
Describes what is happening right now; hypothesis generating
Case-control
Begin with the absence or presence of an outcome and then look backward in time to try to detect possible causes or risk factors
What happened?
Cohort
Begins with the exposure and looks forward longitudinally for the outcome
What will happen?
Study Design
Basic & Clinical Biostatistics (4th Ed).

8. Observational Studies
Study Design
Observational Study Designs
Observational Studies
Study
Design
Population size
Longitudinal
Direction of observation
Comparison Group
Case series
Small
Yes, but short
Prospective No
Cross-sectional
Big
One point in time
Case-control
Yes
Retrospective
Cohort
Usually
Study Design
Basic & Clinical Biostatistics (4th Ed).

9. Commonly reported metrics
Study Design
Commonly reported metrics
P values and CIs
If possible, report both
Confidence intervals tell you everything you need to know
Significance
Idea of the range
RR and ORs
When are they alike?
Risk vs Odds
Significance
NNT/NNH
Resonate with clinicians
Dichotomous data required
Clinical significance vs statistical significance
Study Design

10. Quick reference for formulas
Study Design
Quick reference for formulas
Disease
No Disease
Risk factor present A B
A + B
Risk factor absent C D
C + D
A + C
B + D
Experimental event rate (EER) =
A / (A + B)
Control event rate (CER) =
C/(C + D)
Absolute risk reduction (ARR) =
|EER – CER|
Number needed to treat (NNT) =
1/ARR
Relative risk reduction (RRR) =
|EER-CER|= ARR
CER CER
Relative risk (RR) =
EER= [A/(A+B)]
CER [C/(C+D)]
Odds ratio (OR) =
(A/(A+C)/[C/(A+C)] =A/C =AD
[B/(B+D)]/[D/(B+D)] B/D BC
Study Design
Basic & Clinical Biostatistics (4th Ed).

11. Common pitfalls Study Design Terminology Significance based on CIs
Efficacy vs effectiveness
Significance based on CIs
Crossing 0 or 1 (depending on measurement)
When metrics can be calculated
Type of data
Significance
Study Design

12. Cost Analyses
Types of Analyses
Trends

13. Comparison of methodologies
Cost Analyses
Comparison of methodologies
Methodology
Cost
Outcome
CMA
Dollars
Clinical measure
CEA
CBA
CUA
QALYs
CCA
Multiple
(any of the above)
Cost Analyses

14. A word on cost-effectiveness
Cost Analyses
A word on cost-effectiveness
Nomenclature
Often times articles will contain cost- effectiveness in the title (or text), when in fact it is really another type of cost analyses.
Reporting
Determining the cost-effectiveness threshold is still an issue
Issues with the cost-effectiveness plane
Cost Analyses
Drummond et al (1987).

15. What should be reported
Cost Analyses
What should be reported
Alternatives to the CE plane
Net Health Benefit (NHB)
NHB = QALYs – (Cost/ WTP)
Net Monetary Benefit (NMB)
NMB = QALYs*WTP – Cost
Cost-effectiveness acceptability curve (CEAC)
CEACs
Allow for the comparison of multiple treatment strategies
WTP is unknown, and foreign concept to many health professionals
Cost Analyses

16. Common pitfalls Cost Analyses Terminology Discounting
Type of study
Discounting
Time periods beyond 1 year should be discounted
Deterministic vs Probabilistic
Trend towards probabilistic analyses
Study Design

17. Decision Analysis
The life of a tree
Calculations

18. Anatomy of a tree Chance Node Terminal Node Choice Node
Decision Analysis
Anatomy of a tree
Chance Node
Terminal Node
Choice Node
Decision Analysis

19. Example Example Scenario:
Decision Analysis
Example
Example Scenario:
Given the cost of an antibiotic, the probability of success, the probability of an adverse event, and the cost of treating the adverse event, we can construct a decision tree.
Specify Possible Costs, Outcomes, and Probabilities
For each option, info should be obtained for the probability of occurrence and the consequences of the occurrence
Probabilities are assigned for each branch of the chance nodes, and the sum must add up to 1
Consequences are reported as monetary outcomes, health-related outcomes, or sometimes both
Articles should include a listing of inputs used in the analysis as well as where or how the estimates were obtained
Billomycin
Megacillin
Probability of Clinical Success
90%
80%
Cost of Antibiotic per Course of Therapy
$600
$500
Probability of Adverse Events
10%
15%
Cost of Treating Adverse Events
$1,000
Decision Analysis

20. Example Decision Analysis Decision Analysis
Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins

21. Example Decision Analysis Cost Probability Cost * Probability
Billomycin
Success with no adverse events
$600
0.81
$486
Success with adverse events
$1,600
0.09
$144
Failure with no adverse events
$54
Failure with adverse events
0.01
$16
Total for Billomycin 1
$700
Megacillin
$500
0.68
$340
$1,500
0.12
$180
0.17
$85
0.03
$45
Total for Megacillin
$650 21
Decision Analysis
Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins

22. What to report Decision Analysis
Ideally, a picture of the decision tree is included
Probabilities and Costs should be transparent
Assumptions and sources should be relevant and accessible
Decision Analysis

23. Common pitfalls Transparency Decision Analysis Assumptions Inputs
Sources
Calculations
Decision Analysis

24. Economic Models
Definitions
Trends

25. Evolution of models Economic Models Deterministic Probabilistic
All data is known beforehand
Once you start the process, you know exactly what is going to happen
Example: Predicting the amount of money in a bank account.
If you know the initial deposit and the interest rate, then you can determine the amount of the account after one year
Probabilistic
Element of chance is involved
You know the likelihood that something will happen, but you don’t know when it will happen.
Example: Roll a die until it comes up ‘5’.
In each roll, the probability that it comes up ‘5’ is 1/6
Don’t know exactly when it will be ‘5’, but we can predict this fairly well..
Economic Models

26. Approximately 50- 60% of economic models now are Markov models.
When should you use a Markov model?
A decision tree becomes too complex
The timeframe for the analysis is lengthy
Transitions between health states are possible (e.g. recurrent events)
Modeling a complex disease
Probabilities change over time
Decision models embed Markov processes. Monte Carlo simulations are often used to solve Markov models.
Economic Models

27. What to report Economic Models
At a minimum, the write-up should include:
Type of model
Assumptions
Inputs
Results
Limitations
Economic Models

28. Common pitfalls Economic Models Transparency Terminology Assumptions
Inputs
Sources
Calculations
Terminology
Rate vs probability
Economic Models

29. Conclusions
Lessons learned
Looking ahead

30. Lessons learned Helpful resources Conclusions Conclusions
Conclusions

31. Conclusions
Looking ahead
Conclusions