Budget Impact Modeling: Appropriateness and Determining Quality Input

1. Budget Impact Modeling:Appropriateness and Determining Quality Input C. Daniel Mullins, PhD Professor and Chair, PHSR Dept University of Maryland School of Pharmacy

2. 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?

3. Key Question #1 When is it appropriate to do a BIA? - and when is it not?

4. Appropriate & Inappropriate • Short term models • Lifetime models • Payer perspective • Patient/provider • Cost-effectiveness • Effectiveness

5. Key Question #2 What are criteria for a rigorous BIA?

6. Criteria for a Rigorous BIA Model • Academy of Managed Care Pharmacy (AMCP) Format: Key Elements of a Good Model ~ Structure ~ Data ~ Outputs

7. AMCP Checklist for Good Models: Structure • Transparent • Disease progression model • Relevant timeframe • Appropriate treatment pathways • Good math

8. AMCP Checklist for Good Models: Data • Clinical • Epidemiologic • Cost • Quality of Life • Data quality is critical

9. AMCP Checklist for Good Models: Outputs • Scientific validity • Published in a quality peer-reviewed journal? • Face validity • Do the results make intuitive sense?

10. Key Question #3 What data elements are input into a BIA?

11. Learn by doing: A Case Study • A hypothetical case study for a not so hypothetical new drug

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

13. 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

14. 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

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

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

17. 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)

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

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

30. 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

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

32. 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

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

34. 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

35. 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

36. Key Question #4 How can we ensure quality of BIA models?

37. 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?

38. 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?

39. 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”

40. 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

41. 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”

42. Thank You!