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Budget Impact Modeling: Appropriateness and Determining Quality Input

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Budget Impact Modeling:Appropriateness and Determining Quality Input

C. Daniel Mullins, PhD

Professor and Chair, PHSR Dept

University of Maryland School of Pharmacy

How can we ensure quality of BIA models?

- 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?

When is it appropriate to do a BIA?

- and when is it not?

- Short term models

- Lifetime models

- Payer perspective

- Patient/provider

- Cost-effectiveness

- Effectiveness

What are criteria for a rigorous BIA?

- Academy of Managed Care Pharmacy (AMCP) Format: Key Elements of a Good Model

~ Structure

~ Data

~ Outputs

- Transparent

- Disease progression model

- Relevant timeframe

- Appropriate treatment pathways

- Good math

- Clinical
- Epidemiologic
- Cost
- Quality of Life

- Data quality is critical

- Scientific validity
- Published in a quality peer-reviewed journal?

- Face validity
- Do the results make intuitive sense?

What data elements are input into a BIA?

- 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

Survival

Decision Tree for Selection of Cost-Effective Agent for Hypertension

Mortality

Cost-Effective Agent

Stroke

Survival

Mortality

New drug

Congestive Heart Failure

Survival

Transplant

Renal Failure

No Transplant

No Event

No Intervention

Mortality

Myocardial Infarction

Survival

Mortality

Stroke

Survival

Mortality

Diuretics

Congestive Heart Failure

Survival

The CE ratio of each drug category is evaluated against No Intervention in addition to active comparators

Transplant

Renal Failure

No Transplant

No Event

Cost-Effective Agent

Mortality

No Intervention

Myocardial Infarction

Survival

Mortality

Stroke

Survival

Mortality

No Intervention

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

Results

- 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)

Results

Calculation 3

Calculation 4

Calculation 2

Inputs

Calculation 1

100,000 patients

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

Annual event frequency

Annual total treatment costs

Initial event treatment costs

Annual average event treatment costs

Cumulative costs per event avoided

Results

Calculation 3

Calculation 4

Calculation 2

Inputs

Calculation 1

100,000 patients

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

Annual event frequency

Annual total treatment costs

Initial event treatment costs

Average event probabilities

Annual average event treatment costs

Annual costs per event avoided

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

Average event probabilities calculation example

Calculation done for each drug (D) category and the

No Intervention (NI) category

Results

Calculation 3

Calculation 4

Calculation 2

Inputs

Calculation 1

100,000 patients

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

Annual event frequency

Annual total treatment costs

Initial event treatment costs

Annual persistence adjusted average event probabilities

Annual average event treatment costs

Annual costs per event avoided

Persistence adjusted average event probabilities for year 2 (y2):

PP,Event i,y1 = .8 * PD,A,Event i + .2 * PNI,A,Event i

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

Results

Calculation 3

Calculation 4

Calculation 2

Inputs

Calculation 1

100,000 patients

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

Annual event frequency

Annual total treatment costs

Initial event treatment costs

Annual event frequency

Annual average event treatment costs

Annual costs per event avoided

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

Y2C = 100,000 - EFy1, total events

Year 2 cohort

Results

Calculation 3

Calculation 4

Calculation 2

Inputs

Calculation 1

100,000 patients

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

Annual event frequency

Annual total treatment costs

Initial event treatment costs

Annual total treatment costs

Annual average event treatment costs

Annual costs per event avoided

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]

Results

Calculation 3

Calculation 4

Calculation 2

Inputs

Calculation 1

100,000 patients

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

Annual event frequency

Annual total treatment costs

Initial event treatment costs

Cumulative costs per event avoided

Annual average event treatment costs

Annual costs per event avoided

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

- 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

- 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

- 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

# 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

How can we ensure quality of BIA models?

- 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?

- 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?

- 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”

- Present an overview of your model
- A picture is worth a thousand words
- Walk the decision-maker through the analysis

- BIA should be performed over short to mid-
range time periods – not lifetime

- AMCP guidance focuses on:
- Structure
- Data
- Outputs

- 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”

Thank You!