Transcript Homepage of the Pfizer`s RELPAX® Budget Impact Model

Budget Impact Modeling:
Appropriateness and Determining Quality Input
C. Daniel Mullins, PhD
Professor and Chair, PHSR Dept
University of Maryland School of Pharmacy
 4 Key Questions
 When is it appropriate to do a BIA?
- and when is it not?
 What are criteria for a rigorous BIA?
 What data elements are input into a BIA?
 How can we ensure quality of BIA models?
 Key Question #1
When is it appropriate to do a BIA?
- and when is it not?
Appropriate & Inappropriate
 Short term models
 Lifetime models
 Payer perspective
 Patient/provider
 Cost-effectiveness
 Effectiveness
 Key Question #2
What are criteria for a rigorous BIA?
Criteria for a Rigorous BIA Model
 Academy of Managed Care Pharmacy (AMCP)
Format: Key Elements of a Good Model
~ Structure
~ Data
~ Outputs
AMCP Checklist for Good Models:
Structure
~ Transparent
~ Disease progression model
~ Relevant timeframe
~ Appropriate treatment pathways
~ Good math
AMCP Checklist for Good Models:
Data
 Data quality is critical
~
~
~
~
Clinical
Epidemiologic
Cost
Quality of Life
AMCP Checklist for Good Models:
Outputs
 Face validity
~ Do the results make intuitive sense?
 Scientific validity
~
Published in a quality peer-reviewed journal?
 Key Question #3
What data elements are input into a BIA?
Learn by doing: A Case Study
 A hypothetical case study for a
not so hypothetical new drug
Overview of the presentation of a model
- Presentation of the model
- A walk through the model
- Model assumptions
- Model Limitations
- Take home messages
ACE
ARB
Beta Blockers
CCB
Diuretics
Mortality
Myocardial Infarction
Decision Tree for
Selection of CostEffective Agent
for Hypertension
Survival
Mortality
Cost-Effective Agent
Stroke
Survival
New drug
Mortality
Congestive Heart
Failure
Survival
Transplant
Renal Failure
No Transplant
No Event
No Intervention
Mortality
Myocardial Infarction
Survival
Mortality
Stroke
Survival
Diuretics
The CE ratio of
each drug category
is evaluated
against No
Intervention in
addition to active
comparators
Mortality
Congestive Heart
Failure
Survival
Transplant
Renal Failure
No Transplant
No Event
Cost-Effective Agent
No Intervention
Mortality
Myocardial Infarction
Survival
Mortality
Stroke
Survival
No Intervention
Mortality
Congestive Heart
Failure
Survival
Transplant
Renal Failure
No Transplant
No Event
Overview of the presentation of a model
- Presentation of the model
- A walk through the model
- Model assumptions
- Model Limitations
- Take home messages
Inputs are entered into the model, these are
processed and out comes the costeffectiveness results
Inputs
Results
The model inputs
- Initially 100,000 patients enter the model
- Characteristics of population evaluated in the model
- Event probabilities for each of the possible population groups
evaluated in the model
- Persistency rate for each of the drug treatment categories
- Anti-hypertensive drug treatment costs and office visit costs
- Initial event treatment costs
- Annual average treatment costs after event
(the model runs for 5 years)
Inputs
Calculation 1
Calculation 2
Calculation 3
Calculation 4
Results
100,000 patients
Patient
combination (%)
Caucasian event
probabilities
Average event
probabilities
African
American event
probabilities
Annual
persistency
proportions
HTN drug
treatment costs
and office visit
costs
Initial event
treatment costs
Annual average
event treatment
costs
Annual persistence
adjusted average
event probabilities
Annual event
frequency
Annual total
treatment costs
Cumulative costs
per event avoided
Calculation 1
100,000Inputs
patients
Calculation 1
Calculation 2
Calculation 3
Results
Calculation 4
Patient combination
(%)
Caucasian event
probabilities
Average event
probabilities
African American
event probabilities
Annual persistency
proportions
Annual persistence
adjusted average event
probabilities
HTN drug treatment
costs and office visit
costs
Initial event treatment
costs
Annual average event
treatment costs
Average event probabilities
Annual event
frequency
Annual total treatment
costs
Annual costs per event
avoided
Average event probabilities calculation example
Calculation done for each drug (D) category and the
No Intervention (NI) category
Input 70% Caucasian (C) and 30%African American (AA):
Calculation done for each event i
NI Average Event i Probability
PNI,A,Event i= .7 * PNI,C,Event i + .3 * PNI,AA,Event i
Drug Average Event i Probability
PD,A,Event i = .7 * PD,C,Event i + .3 * PD,AA,Event i
Calculation 2
100,000Inputs
patients
Calculation 1
Calculation 2
Calculation 3
Results
Calculation 4
Patient combination
(%)
Caucasian event
probabilities
Average event
probabilities
African American
event probabilities
Annual persistency
proportions
HTN drug treatment
costs and office visit
costs
Initial event treatment
costs
Annual average event
treatment costs
Annual persistence
adjusted average event
probabilities
Annual event
frequency
Annual total treatment
costs
Annual costs per event
avoided
Annual persistence adjusted average event probabilities
Persistence adjusted average event probabilities
calculation example
Calculation done for each year, since persistence can change
from year to year
Input for year 2: 80% fully persistent, 20% not persistent
Persistence adjusted average event probabilities for year 2 (y2):
PP,Event i,y1 = .8 * PD,A,Event i + .2 * PNI,A,Event i
Calculation 3
100,000Inputs
patients
Calculation 1
Calculation 2
Calculation 3
Results
Calculation 4
Patient combination
(%)
Caucasian event
probabilities
Average event
probabilities
African American
event probabilities
Annual persistency
proportions
HTN drug treatment
costs and office visit
costs
Initial event treatment
costs
Annual average event
treatment costs
Annual persistence
adjusted average event
probabilities
Annual event
frequency
Annual total treatment
costs
Annual costs per event
avoided
Annual event frequency
Event frequency (EF)
Calculation done for each year, since persistence change
and so does the cohort size
Event frequency for year 1
Event frequency for year 1, Event i
EFy1,Event i = 100,000 * PP,Event i,y1
Number of Event i deaths year 1
# Event i deaths in year 1
# Dy1,Event i = EFy1,Event i * Event i Mortality rate
Number of Event i survivors in year 1
# Event i survivors in year 1
# Sy1,Event i = EFy1,Event i - # Dy1,Event i
Size of year 2 cohort
Year 2 cohort
Y2C = 100,000 - EFy1, total events
Calculation 4
100,000Inputs
patients
Calculation 1
Calculation 2
Calculation 3
Results
Calculation 4
Patient combination
(%)
Caucasian event
probabilities
Average event
probabilities
African American
event probabilities
Annual persistency
proportions
HTN drug treatment
costs and office visit
costs
Initial event treatment
costs
Annual average event
treatment costs
Annual persistence
adjusted average event
probabilities
Annual event
frequency
Annual total treatment
costs
Annual costs per event
avoided
Annual total treatment costs
Annual total treatment costs
Calculation done for each year, since event frequency change
over time due to the decreasing cohort size
Year 1 total treatment costs
TCy1,event i =[EFy1,event i * Event i initial costs] +
[100,000 * yearly Drug/Office visit costs]
Year 2 total treatment costs
TCy2,event i =[EFy2,event i * Event i initial costs] +
[Y2C * yearly Drug/Office visit costs] +
[# Sy1,Event i * Year 1 Event i average event treatment costs]
Calculation 5
100,000Inputs
patients
Calculation 1
Calculation 3
Calculation 2
Results
Calculation 4
Patient combination
(%)
Caucasian event
probabilities
Average event
probabilities
African American
event probabilities
Annual persistency
proportions
HTN drug treatment
costs and office visit
costs
Initial event treatment
costs
Annual average event
treatment costs
Annual persistence
adjusted average event
probabilities
Annual event
frequency
Annual total treatment
costs
Annual costs per event
avoided
Cumulative costs per event avoided
Cumulative costs per event avoided
Calculation done for each drug treatment category
evaluated
Cumulative costs per event avoided for a drug treatment category
CPEA = [ TCy1, all events, NI - TCy1,all events, drug treatment]
[#EFy1,all events, NI - #EFy1,all events, drug treatment]
- The lower the “costs per event avoided” the better
Overview of the presentation of a model
- Presentation of the model
- A walk through the model
- Model assumptions
- Model Limitations
- Take home messages
Model assumptions
- The baseline event probabilities represents an average American
hypertensive population (age, gender, co-morbidities)
- Same annual event probability applied each model year
- Same event survival probability applied to each treatment category
- Immediate effect of drug treatment persistency status
- Once patients become non persistent with drug treatment, they stay so
- Same annual office visit costs across treatment categories
- Linear event treatment costs interpolated from missing data
Overview of the presentation of a model
- Presentation of the model
- A walk through the model
- Model assumptions
- Model Limitations
- Take home messages
Limitations
- Future events modeled by down stream event treatment costs
- Patients with multiple factors are not considered in the model (LVH/diab.)
- Average event treatment costs may not be constant in years after the event
- Partial drug treatment persistency is not considered
- Drug treatment switch is not considered
Overview of the presentation of a model
- Presentation of the model
- A walk through the model
- Model assumptions
- Model Limitations
- Take home messages
Take Home Messages
- Drug A reduces DBP by x mm HG and SPB by y mm Hg
- Drug A provides a favorable safety profile
- Drug A improves patient functioning based on physical domain of ABC
- Drug A reduces down stream event treatment costs
Lessons learned and tricks of the trade
# 1 Be transparent
# 2 Describe limitations (see #1)
# 3 Describe the model in a simple form (see #1)
# 4 Get to the point
# 5 Stick to the point
 Key Question #4
How can we ensure quality of BIA models?
Testing the quality
 Test for face validity
~ Do the results make intuitive sense?
~ Do the results seem believable?
 Try to “break the model”
~ Put in “outlier” values
~ Does the model “explode”?
~ Does the model always give the same result?
Ensuring the quality
 Consider local practice patterns
~ Local prevalence
~ Compare to “standard of care”
~ Use inputs that reflect local
 Costs
 Hospital length of stay
 Physician practices
 Allow for Plan-specific values
~ Do the results reflect Plan demographics?
~ Do the results reflect Plan costs?
Provide transparent inputs and
results so that decision-maker can
 Perform their own assessment
 Feel comfortable with assumptions
 Feel comfortable with inputs
 Feel comfortable with calculations
 Feel comfortable with what’s in the
“black box”
Summary
 BIA should be performed over short to midrange time periods – not lifetime
 AMCP guidance focuses on:
~ Structure
~ Data
~ Outputs
 Present an overview of your model
~ A picture is worth a thousand words
~ Walk the decision-maker through the analysis
Conclusion
 BIA should reflect the appropriate perspective
and what they care about
 BIA calculations should be transparent and
provide insight into change in costs:
~ Drug Costs
~ Total Medical Costs
 Make the user interface user friendly
 Allow the decision-maker to see or understand
what’s in the “black box”