Terminology and Jargon
Demystified
Meg Franklin, PharmD, PhD
Franklin Pharmaceutical Consulting
March 27, 2015
1
table of contents
Intro
01
02
Health Econ 101
Why are we here?
Study Design
Types of studies
Metrics to report
Cost Analyses
suspects
Usual
Trends
03
04
05
Decision Analysis
The life of a tree
Calculations
Economic Models
Definitions
Trends
Conclusions
Lessons learned
Looking ahead
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 decisionmaker to take out the old light bulb and put in a new one.
3
Introduction
Why are we here?
 Improve writing
 Communicate effectively
 Share ideas and experiences
 Develop strategies for publications
Focus on:
1. Nomenclature
2. What to report
3. Common pitfalls
Calvin and Hobbes, by Bill Watterson
Introduction
4
Study
Design
Types of Studies
Metrics to Report
Study Design
Observational study designs
Crosssectional
Case-Control
Today
Cohort
Historical Control
Study Design
Adapted from Figure 2-5 in Basic & Clinical Biostatistics (4th Ed).
6
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).
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Study Design
Observational
Studies
Observational
Study
Study
Design
Designs
Population size
Longitudinal
Direction of
observation
Comparison Group
Small
Yes, but short
Prospective
No
Cross-sectional
Big
No
One point in time
No
Case-control
Big
Yes
Retrospective
Yes
Cohort
Big
Yes
Prospective
Usually
Case series
Study Design
Basic & Clinical Biostatistics (4th Ed).
8
Study Design
Commonly reported metrics
P values and CIs
RR and ORs
NNT/NNH

If possible, report both

When are they alike?

Resonate with clinicians

Confidence intervals tell
you everything you need to
know

Risk vs Odds


Significance
Dichotomous data
required

Clinical significance vs
statistical significance
• Significance
• Idea of the range
Study Design
9
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) =
Number needed to treat (NNT) =
Relative risk reduction (RRR) =
Relative risk (RR) =
Odds ratio (OR) =
|EER – CER|
1/ARR
|EER-CER|= ARR
CER CER
EER= [A/(A+B)]
CER [C/(C+D)]
(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).
10
Study Design
Common pitfalls
Terminology

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
11
Cost
Analyses
Types of Analyses
Trends
Cost Analyses
Comparison of methodologies
Methodology
Cost
Outcome
CMA
Dollars
Clinical measure
CEA
Dollars
Clinical measure
CBA
Dollars
Dollars
CUA
Dollars
QALYs
CCA
Dollars
Multiple
(any of the above)
Cost Analyses
13
Cost Analyses
A word on cost-effectiveness
Nomenclature
Often times articles will contain costeffectiveness 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).
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Cost Analyses
What should be reported
Alternatives to the CE plane
Net Health Benefit (NHB)


Net Monetary Benefit (NMB)



NHB = QALYs – (Cost/ WTP)
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
http://www.jmir.org/article/viewFile/2059/1/21640
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Cost Analyses
Common pitfalls
Terminology

Type of study
Discounting

Time periods beyond 1 year should be discounted
Deterministic vs Probabilistic

Trend towards probabilistic analyses
Study Design
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Decision
Analysis
The life of a tree
Calculations
Decision Analysis
Anatomy of a tree
Chance Node
Terminal Node
Choice Node
Decision Analysis
18
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.
Billomycin
Megacillin
Probability of Clinical Success
90%
80%
Cost of Antibiotic per Course of Therapy
$600
$500
Probability of Adverse Events
10%
15%
$1,000
$1,000
Cost of Treating Adverse Events
Decision Analysis
19
Decision Analysis
Example
Decision Analysis
Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins. 2009.
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Decision Analysis
Example
Cost
Probability
Cost * Probability
Billomycin
Success with no adverse events
Success with adverse events
Failure with no adverse events
Failure with adverse events
Total for Billomycin
$600
$1,600
$600
$1,600
0.81
0.09
0.09
0.01
1
$486
$144
$54
$16
$700
Megacillin
Success with no adverse events
Success with adverse events
Failure with no adverse events
Failure with adverse events
Total for Megacillin
$500
$1,500
$500
$1,500
0.68
0.12
0.17
0.03
1
$340
$180
$85
$45
$650
21
Decision Analysis
Rascati KL. Essentials of Pharmacoeconomics. Philadelphia, PA: Lippencott Williams & Wilkins. 2009.
21
Decision Analysis
What to report
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
22
Decision Analysis
Common pitfalls
Transparency

Assumptions

Inputs

Sources

Calculations
Decision Analysis
23
Economic
Models
Definitions
Trends
Economic Models
Evolution of models
Deterministic
Probabilistic
All data is known beforehand
Element of chance is involved
Once you start the process, you know exactly what is going
to happen
You know the likelihood that something will happen, but
you don’t know when it will 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
 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
http://people.qc.cuny.edu/faculty/christopher.hanusa/courses/245sp11/Documents/245ch5-3.pdf
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Economic Model
Markov models
When should you use a Markov model?
Approximately
5060%
of
economic models now
are Markov models.

A decision tree becomes too complex

The timeframe for the analysis is lengthy

Transitions between health states are possible
recurrent events)

Modeling a complex disease

Probabilities change over time
(e.g.
Decision models embed Markov processes. Monte Carlo simulations
are often used to solve Markov models.
Economic Models
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Economic Models
What to report
At a minimum, the write-up should include:

Type of model

Assumptions

Inputs

Results

Limitations
Economic Models
http://www.spandidos-publications.com/article_images/mco/1/1/MCO-01-01-0175-g00.jpg
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Economic Models
Common pitfalls
Transparency

Assumptions

Inputs

Sources

Calculations
Terminology

Rate vs probability
Economic Models
28
Conclusions
Lessons learned
Looking ahead
Conclusions
Lessons learned
Helpful resources
http://www.pharmacy.arizona.edu/
centers/hope/training-programs
Conclusions
30
Conclusions
Looking ahead
Conclusions
31