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?  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? - and when is it not?

 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 ~ Clinical ~ Epidemiologic ~ Cost ~ Quality of Life  Data quality is critical

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 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 not so hypothetical new drug

- Presentation of the model - Presentation of the model - A walk through the model - A walk through the model - Model assumptions - Model Limitations Overview of the presentation of a model - Take home messages

Decision Tree for Selection of Cost- Effective Agent for Hypertension ACE ARB Beta Blockers CCB Diuretics Mortality Survival Myocardial Infarction Mortality Survival Stroke Congestive Heart Failure Transplant No Transplant Renal Failure No Event New drug Cost-Effective Agent No Intervention Mortality Survival

The CE ratio of each drug category is evaluated against No Intervention in addition to active comparators Diuretics Mortality Survival Myocardial Infarction Mortality Survival Stroke Congestive Heart Failure Transplant No Transplant Renal Failure No Event Cost-Effective Agent No Intervention Mortality Survival Mortality Survival Myocardial Infarction Mortality Survival Stroke Congestive Heart Failure Transplant No Transplant Renal Failure No Event Mortality Survival No Intervention

- Presentation of the model - Presentation of the model - A walk through the model - A walk through the model - Model assumptions - Model Limitations Overview of the presentation of a model - Take home messages

Inputs are entered into the model, these are processed and out comes the cost- effectiveness 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 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) (the model runs for 5 years)

100,000 patients Patient combination (%) Caucasian 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 Inputs Average event probabilities Calculation 1 Annual persistence adjusted average event probabilities Calculation 2 Annual event frequency Calculation 3 Annual total treatment costs Calculation 4 Cumulative costs per event avoided Results

Calculation 1 Average event probabilities Annual persistence adjusted average event probabilities Annual event frequency Annual total treatment costs Annual costs per event avoided 100,000 patients Patient combination (%) Caucasian 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 Inputs Calculation 1 Calculation 2 Calculation 3 Calculation 4 Results Average event probabilities

Input 70% Caucasian (C) and 30%African American (AA): Calculation done for each event i Drug Average Event i Probability P D,A,Event i =.7 * P D,C,Event i +.3 * P D,AA,Event i NI Average Event i Probability P NI,A,Event i =.7 * P NI,C,Event i +.3 * P NI,AA,Event i Average event probabilities calculation example Calculation done for each drug (D) category and the No Intervention (NI) category

Calculation 2 Average event probabilities Annual persistence adjusted average event probabilities Annual event frequency Annual total treatment costs Annual costs per event avoided 100,000 patients Patient combination (%) Caucasian 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 Inputs Calculation 1 Calculation 2 Calculation 3 Calculation 4 Results 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 Persistence adjusted average event probabilities for year 2 (y2): P P,Event i,y1 =.8 * P D,A,Event i +.2 * P NI,A,Event i Input for year 2: 80% fully persistent, 20% not persistent

Calculation 3 Average event probabilities Annual persistence adjusted average event probabilities Annual event frequency Annual total treatment costs Annual costs per event avoided 100,000 patients Patient combination (%) Caucasian 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 Inputs Calculation 1 Calculation 2 Calculation 3 Calculation 4 Results Annual event frequency

Event frequency (EF) Calculation done for each year, since persistence change and so does the cohort size # Dy1,Event i = EFy1,Event i * Event i Mortality rate Number of Event i deaths year 1 # Event i deaths in year 1 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 Event frequency for year 1, Event i EFy1,Event i = 100,000 * PP,Event i,y1 Event frequency for year 1

Calculation 4 Average event probabilities Annual persistence adjusted average event probabilities Annual event frequency Annual total treatment costs Annual costs per event avoided 100,000 patients Patient combination (%) Caucasian 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 Inputs Calculation 1 Calculation 2 Calculation 3 Calculation 4 Results 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 TC y1,event i =[EF y1,event i * Event i initial costs] + [100,000 * yearly Drug/Office visit costs] TC y2,event i =[EF y2,event i * Event i initial costs] + [Y2C * yearly Drug/Office visit costs] + [# S y1,Event i * Year 1 Event i average event treatment costs] Year 2 total treatment costs

Calculation 5 Average event probabilities Annual persistence adjusted average event probabilities Annual event frequency Annual total treatment costs Annual costs per event avoided 100,000 patients Patient combination (%) Caucasian 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 Inputs Calculation 1 Calculation 2 Calculation 3 Calculation 4 Results Cumulative costs per event avoided

Cumulative costs per event avoided Calculation done for each drug treatment category evaluated CPEA = [ TC y1, all events, NI - TC y1,all events, drug treatment ] [#EF y1,all events, NI - #EF y1,all events, drug treatment ] Cumulative costs per event avoided for a drug treatment category - The lower the “costs per event avoided” the better

- Presentation of the model - Presentation of the model - A walk through the model - A walk through the model - Model assumptions - Model Limitations - Take home messages Overview of the presentation of a model

Model assumptions - The baseline event probabilities represents an average American hypertensive population (age, gender, co-morbidities) - Immediate effect of drug treatment persistency status - Once patients become non persistent with drug treatment, they stay so - Linear event treatment costs interpolated from missing data - Same event survival probability applied to each treatment category - Same annual event probability applied each model year - Same annual office visit costs across treatment categories

- Presentation of the model - Presentation of the model - A walk through the model - A walk through the model - Model assumptions - Model Limitations - Take home messages Overview of the presentation of a model

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

- Presentation of the model - Presentation of the model - A walk through the model - A walk through the model - Model assumptions - Model Limitations - Take home messages Overview of the presentation of a model

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  Try to “break the model” ~ Put in “outlier” values ~ Does the model “explode”? ~ Does the model always give the same result?  Test for face validity ~ Do the results make intuitive sense? ~ Do the results seem believable?

Ensuring the quality  Allow for Plan-specific values ~ Do the results reflect Plan demographics? ~ Do the results reflect Plan costs?  Consider local practice patterns ~ Local prevalence ~ Compare to “standard of care” ~ Use inputs that reflect local  Costs  Hospital length of stay  Physician practices

Provide transparent inputs and results so that decision-maker can Perform their own assessment  Perform their own assessment Feel comfortable with assumptions  Feel comfortable with assumptions Feel comfortable with inputs  Feel comfortable with inputs Feel comfortable with calculations  Feel comfortable with calculations Feel comfortable with what’s in the  Feel comfortable with what’s in the “black box” “black box”

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-  BIA should be performed over short to mid- range time periods – not lifetime range time periods – not lifetime  AMCP guidance focuses on: ~ Structure ~ Data ~ Outputs

Conclusion  BIA should reflect the appropriate perspective and what they care about and what they care about  BIA calculations should be transparent and provide insight into change in costs: 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” what’s in the “black box”