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.  4 Key Questions.  How can we ensure quality of BIA models?. When is it appropriate to do a BIA?

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Budget impact modeling appropriateness and determining quality input

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

4 Key Questions

 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?


Key question 1

Key Question #1

When is it appropriate to do a BIA?

- and when is it not?


Appropriate inappropriate

Appropriate & Inappropriate

  • Short term models

  • Lifetime models

  • Payer perspective

  • Patient/provider

  • Cost-effectiveness

  • Effectiveness


Key question 2

Key Question #2

What are criteria for a rigorous BIA?


Criteria for a rigorous bia model

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

AMCP Checklist for Good Models: Structure

  • Transparent

  • Disease progression model

  • Relevant timeframe

  • Appropriate treatment pathways

  • Good math


Amcp checklist for good models data

AMCP Checklist for Good Models: Data

  • Clinical

  • Epidemiologic

  • Cost

  • Quality of Life

  • Data quality is critical


Amcp checklist for good models outputs

AMCP Checklist for Good Models: Outputs

  • Scientific validity

    • Published in a quality peer-reviewed journal?

  • Face validity

    • Do the results make intuitive sense?


Key question 3

Key Question #3

What data elements are input into a BIA?


Learn by doing a case study

Learn by doing: A Case Study

  • A hypothetical case study for a

    not so hypothetical new drug


Budget impact modeling appropriateness and determining quality input

Overview of the presentation of a model

- Presentation of the model

- A walk through the model

- Model assumptions

  • Model Limitations

- Take home messages


Budget impact modeling appropriateness and determining quality input

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


Budget impact modeling appropriateness and determining quality input

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


Budget impact modeling appropriateness and determining quality input

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 cost effectiveness results

Inputs

Results

Inputs are entered into the model, these are processed and out comes the cost-effectiveness results


The model inputs

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)


Budget impact modeling appropriateness and determining quality input

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


Calculation 1

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

Calculation 1


Budget impact modeling appropriateness and determining quality input

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


Calculation 2

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

Calculation 2


Budget impact modeling appropriateness and determining quality input

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


Calculation 3

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

Calculation 3


Budget impact modeling appropriateness and determining quality input

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

Event frequency (EF) Calculation done for each year, since persistence change and so does the cohort size


Calculation 4

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

Calculation 4


Budget impact modeling appropriateness and determining quality input

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]

Annual total treatment costs Calculation done for each year, since event frequency change over time due to the decreasing cohort size


Calculation 5

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

Calculation 5


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]

Cumulative costs per event avoidedCalculation done for each drug treatment category evaluated

- The lower the “costs per event avoided” the better


Budget impact modeling appropriateness and determining quality input

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

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


Budget impact modeling appropriateness and determining quality input

Overview of the presentation of a model

- Presentation of the model

- A walk through the model

- Model assumptions

  • Model Limitations

- Take home messages


Limitations

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


Budget impact modeling appropriateness and determining quality input

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

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

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

Key Question #4

How can we ensure quality of BIA models?


Testing the quality

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

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

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

Summary

  • 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


Conclusion

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”


Budget impact modeling appropriateness and determining quality input

Thank You!


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