ISPOR 19 th International Meeting May 31 st June 4 th 2014, Montréal International Society for Pharmacoeconomics and Outcomes Research

Transcription

1 ISPOR 19 th International Meeting May 31 st June 4 th 2014, Montréal International Society for Pharmacoeconomics and Outcomes Research Pascale Boyer Barresi, CFA Business Analysis, BD&L September 2014

2 Topics Pharmaceutical Pricing Risk Sharing & Performance-Based Agreements Use of Big Data & Healthcare Analytics Changing the Drug Development Paradigm European HTA Collaboration Health Economics in the Context of Personalized Medicine Valuing Targeted Therapies Market Access Modelization Value-based Pricing across Indications 2 2

3 Pharmaceutical Pricing 3 3

4 Pharmaceutical Pricing Research Process Preclinical -36 months -24 months -12 months Launch Post Launch Secondary research: establish ranges, hypotheses and monitor the market Pricing Choices Qualitative research: narrow range, understand dynamics of decision making and fine tune Quantitative research: specify the role and importance of price, enumerate trade-offs Premium: class (re)-definition = higher than current competition = unique & superior value proposition; sufficiently higher to be noticeable Parity: similar to current competition (tactically slightly higher or lower) Penetration: discount = within reach of current competition, sufficiently lower to be noticeable OR low-ball = far lower than competition, implying a value shift (generics) 4 4

5 Pharmaceutical Pricing What type of price? Public price (for cash payers) Negotiated price (reimbursed price) Hospital price Ex-factory price (without discounts) Net selling price (ExFact with discounts) Where we stand to forecast our revenue base Adjust your mindset and perspective to who you are talking to. Ex: for sales forecasts, you will use the net selling price. For pharmacoeconomic analysis, you will use the reimbursed price. Pricing terminology is very often country specific. Price has to be understood properly between affiliate and headquarters. 5 5

6 Pharmaceutical Pricing Focus Player Clinicians Payers Patients Pharma Will focus on: Clinical effects Cost (total budget) & HTA Out of pocket cost & effects Science The best use of pharmacoeconomics is to determine the value of medications in use. This will help you define where you will generate most of the value. Pharmacoeconomics should: Drive clinical research protocols Describe important aspects of the market Inform and guide pricing Help customers comprehend value and use products efficiently & effectively. 6 6

7 Pharmaceutical Pricing Drivers in determining price vary based on the disease state and therapy offered Public Policy Environment Competitive Environment Reimbursement Environment Patient & Disease Characteristics Decision Making Value Product Strategy Company Needs & Abilities Price 7 7

8 Pharmaceutical Pricing Valuation and pricing activities Early Development Financial model of disorder(s) Identification of leverage points & critical success factors Phase II & III Measurement in trials (endpoints, comparators, ) Develop & refine pricing scenarii Launch Communication of value Preclinical & Phase I Identification of differential value, initial pricing estimates, scenarii & analysis Pre-launch Refine and test price levels, scenarii & value proposition Final pricing strategy (list & tactical) decisions Postlaunch Ongoing price management 8 8

9 Pharmaceutical Pricing Pricing and reimbursement are linked together. Decisions are based on perceived value. Value perceptions are based on both qualitative and quantitative factors. Decision making processes are influenced by the context. Key questions: Who decides about the price of a pharmaceutical product? How? Manufacturers, health authorities, formal evaluation, contracting, Who decides about the listing and reimbursement? How? National and/or local health authorities, formal evaluation, contracting, Who decides about the utilization? How? Individual physicians, professional guidelines, national and/or local health authorities, evidence-based, patients preferences, Who pays the price? How? Private payers, public payers, patients, insurance premiums, co-payments, fixed budgets, 9 9

10 Pharmaceutical Pricing Pricing takes place before launch in some countries and after launch in others

11 Pharmaceutical Pricing Pricing Strategy Process Business Strategy Pricing & Reimbursement Strategy Payer/Managed Markets Strategy Build & Execute Plan Identify options to explore Perform gap analysis Ensure alignment Model & Test Strategies Design & test programs Ensure «real world» evaluations Analyze & offer refinements Implementation Decisions Strategies & programs Tactics & targets Metrics for evaluation Educate for execution Implementation Execute consumer communication Evaluate progress vs. metrics Refine strategies & tactics 11 11

12 Risk-Sharing & Performance-Based Agreements 12 12

13 Performance-Based & Risk Sharing Agreements Pay for performance Old concept 13 13

14 Performance-Based & Risk Sharing Agreements Basics in health economics: Drugs are approved, launched & reimbursed under conditions of uncertainty affecting: Efficacy Effectiveness (real world) Risk Models (links between surrogate markers & long term outcomes) Cost effectiveness Budget impact (affordability) Gathering more evidence is costly Biology Improvement in comorbidities Glucose Cholesterol Clinical Improved clinical outcomes Cardiovascular Cerebrovascular Economics Better health outcomes Length of life Quality of life 14 14

15 Performance-Based & Risk Sharing Agreements Key Characteristics of PBRSA: 1. Program of data collection agreed between the payer and the manufacturer 2. Data collection is starting right after the regulatory approval 3. Pricing, reimbursement & revenue for the product are linked to the outcome of this data collection 4. The goal of the data collection is to address uncertainty (efficacy in a specific population, efficacy in a broader population, ) 5. The distribution of risk is different between the payer and the manufacturer than the historical relationship Understanding the real outcomes could help predict long-term adoption and impact 15 15

16 Performance-Based & Risk Sharing Agreements Payer response to increasing cost pressures: Increasing patient co-payment Pre-use authorization Quantity and dose limitation Benefit restrictions Denial of coverage Performance-based & risk sharing agreements 16 16

17 Performance-Based & Risk Sharing Agreements What are the options of the payer? Payer options Yes Payer adopts: no new evidence required No Payer refuses to adopt Yes but Payer adopts with additional evidence (CED) Manufacturer has the option to reapply with more evidence Use only in research CED (Coverage with Evidence Development) with renegotiation. No prespecified agreement CED linked to performance agreement 17 17

18 Performance-Based & Risk Sharing Agreements PBRSA Taxonomies several references but key dimensions Patient Level Population Level Non-outcome based Utilization capitations Discounted treatment initiation Fixed cost per patient Market share Price-volume Expenditure/budget cap Price change/discount Outcome based Price linked to per patient outcome Conditional treatment continuation Money-back guarantee Only with research (coverage with evidence development) using observational study or RCT Only in research (patients only get access if they agree to participate in a study) 18 18

19 Performance-Based & Risk Sharing Agreements Practical difficulties: Transaction costs Development of processes Health personnel time to administer the scheme Limitation of current medical information systems to measure and track performance EMR are sometimes not implemented in all countries Operational Agreeing on the scheme details Identification of eligible patients and relevant clinical outcomes and/or therapeutic goals Measurement errors with validating and confirming clinical end points (scales for clinical measurement) How are competitor products treated? 19 19

20 Performance-Based & Risk Sharing Agreements Number of Active Performance-based & Risk-sharing Agreements by country (2014) Coverage with Evidence Development Conditional Treatment Continuation Performance-Linked Reimbursement 20 20

21 Use of Big Data & Healthcare Analytics 21 21

22 Use of Big Data & Healthcare Analytics 22 22

23 Use of Big Data & Healthcare Analytics 23 23

24 Use of Big Data & Healthcare Analytics Traditional methods vs. Machine-learning methods to extract the best from Big Data Traditional methods Good methods for developing well-matched control groups but no magic bullets These methods control only for observables but do not for endogeneity or confounding. Human intervention can biased classification. These methods can be used for small datasets. Machine-learning methods Many methods with the same basic approach Basic approach: Use learning datasets to develop highly accurate classification algorithm. Apply algorithm to another dataset to predict classification. Rules should be as simple as possible while maintaining accuracy. Should be able to classify data without human intervention Should be efficient with very large datasets Some machine learning methods use regression methods with a penalty term to adjust for the danger of overfitting (spurious correlations)

25 Use of Big Data & Healthcare Analytics 25 25

26 Use of Big Data & Healthcare Analytics Summary Rapid expansion in data (volume, velocity, and variety) Machine learning approaches focus on prediction but some can also be used to estimate treatment effects Machine learning methods offer opportunities for speed to answer but traditional challenges with observational data do not go away More data doesn t help with bias problems unless it helps with control variables through data linkage For treatment effect estimation still need to think about possible sources of bias and their implications for methodology and data used for model building 26 26

27 Use of Big Data & Healthcare Analytics 27 27

28 Use of Big Data & Healthcare Analytics 28 28

29 Use of Big Data & Healthcare Analytics 29 29

30 Use of Big Data & Healthcare Analytics 30 30

31 Use of Big Data & Healthcare Analytics The outcome research process Inform clinical practice Define the question Exploratory vs. confirmatory Grounded in clinical practice, policy Builds on current knowledge Guided by data Design the study Interpret the results Data may not reflect a random sample of patients Reference standards for prediction may be inadequate Measurement of concepts, coding Debiopharm changes International over time SA confidential Analyze Prepare the data Fragmentation of health care (and data) Completeness and accuracy of data Standardization of data across organizations Understand the workflow that generated the data Identify and address data quality problems Missingness Inconsistencies Engage data partners and experts

32 Use of Big Data & Healthcare Analytics 32 32

33 Use of Big Data & Healthcare Analytics 33 33

34 Use of Big Data & Healthcare Analytics 34 34

35 Use of Big Data & Healthcare Analytics 35 35

36 Use of Big Data & Healthcare Analytics 36 36

37 Use of Big Data & Healthcare Analytics Health Plan Claims Inpatient Chart Information Outpatient Chart Information 37 37

38 Big Data Barriers & Opportunities EVALUATE Data show what works and what doesn t ADJUST Evidence influences continual improvement In a learning healthcare system, research influences practice and practice influences research. IMPLEMENT Researchers collect data from pilot and control settings DESIGN Clinicians and researchers design care based on evidence Innovation is disseminated to improve care throughout the system 38 38

39 Changing the Drug Development Paradigm 39 39

40 Changing the Drug Development Paradigm 40 40

41 Changing the Drug Development Paradigm 41 41

42 Changing the Drug Development Paradigm 42 42

43 Changing the Drug Development Paradigm What are the key points of this new paradigm? Greater acceptability of enrichment designs and surrogate endpoints for regulatory approval Patient-powered research networks and country-sponsored registries Selected pockets of healthcare systems and some countries with reliable mechanisms to track patients healthcare use across settings of care and longitudinally through clinically-rich electronic health records Greater harmonization between regulatory agencies and HTA bodies in Europe (we will talk about that in the next section) 43 43

44 European HTA Collaboration 44 44

45 European HTA collaboration ICT: Information and Communication Technology 45 45

46 European HTA collaboration 46 46

47 European HTA collaboration 47 47

48 European HTA collaboration 48 48

49 Health Economics in the Context of Personalized Medicine 49 49

50 Health Eco & Personalized Medicine Personalized Medicine A form of medicine that uses information about a person s genes, proteins, and environment to prevent, diagnose, and treat disease, or to make a prognosis Also called precision medicine 50 50

51 Health Eco & Personalized Medicine Key Policy Issues Evidence gaps Most tests come to market without FDA review/approval Variable use in practice Tests diffuse into practice unevenly Many patients who could benefit are not offered the test Off-indication use is common Uneven and uncertain payment structure Test-specific billing codes are often unavailable Insurer policies towards reimbursement can vary widely Lack of evidence of cost-effectiveness Models, when they exist, often rely on registration data even though real world use may be very different from ideal use

52 Health Eco & Personalized Medicine Pro Evidence-based personalized medicine: is PM a micro version of EBM? What evidence should payers require? Payer challenges in evaluating evidence: small patient numbers, new methodologies (WGS), pricing and affordability, how much can we generalize results? How to demonstrate clinical utility? AMCP criteria: analytical validity, clinical validity, clinical utility, cost effectiveness (see next slide) CEA is relevant and important Efficiency is achieved under specific conditions (see next slide) Against Traditional health economic approaches to framing and assessing PM have had limited success addressing specific issues Uncertainty in economic analyses of PM applications precludes effective use by policymakers Inherent value of PM information to patients personal utility is not explicitly evaluated Influence of PM attributes on uptake and thus population impact is not captured CEA too slow WGS: Whole Genome Sequencing AMCP: Academy of Managed Care Pharmacy

53 Health Eco & Personalized Medicine 53 53

54 Health Eco & Personalized Medicine 54 54

55 Health Eco & Personalized Medicine CEA is relevant and important Not all biomarker tests are worth doing Need to manage efficient use of this technology Answers are not always straightforward Efficiency is achieved when: The drug is narrowly targeted (benefits only a small subset of potential patients) Test results are actionable to providers and patients Testing substantially improves clinical outcomes Drug cost is large compared to diagnostic cost 55 55

56 Valuing Targeted Therapies 56 56

57 Valuing Targeted Therapies Getting the question right (1/2) How is test used in clinical practice? Diagnostic or screening Triage, replacement, add-on Cut-point used If combined with another test, how is this done (eg IHC 2+ & FISH +ve = positive?) Does the test result actually change patient management? How will the test result be acted upon? Positives Negatives Equivocals Unreadables Could vary considerably between countries (& within countries!) 57 57

58 Valuing Targeted Therapies Getting the question right (2/2) Who uses the test and where? Home, GP, Spec, Emergency Dept Near-patient or lab Who gets the test? The symptomatic patient only? Does it then prompt screening of others +siblings, +contacts Who benefits from the test? In the broadest sense What are the health sector implications? What are the insurance implications? To the patient (and relatives!) Is this test result of value to others? Eg. other targeted therapies Is other information gained at the same time? 58 58

59 Valuing Targeted Therapies 59 59

60 Valuing Targeted Therapies 60 60

61 Valuing Targeted Therapies Important and unanswered questions about the real world use of targeted therapies cannot be answered with hypothetical cohort simulations informed primarily by data from trials: - Which patients get tested and treated? - How accurate is testing in the clinical setting? - What testing and treatment approaches are used to direct targeted therapy in actual clinical practice?

62 Valuing Targeted Therapies 62 62

63 Valuing Targeted Therapies 63 63

64 Valuing Targeted Therapies 64 64

65 Valuing Targeted Therapies GEP: Gene Expression Profiling 65 65

66 Market Access Modelization 66 66

67 Market Access Modelization Context: MCDM/MCDA: Multi-Criteria Decision Roche for Market access & HTA modelization 67 67

68 Market Access Modelization 68 68

69 Market Access Modelization 69 69

70 Market Access Modelization 70 70

71 Market Access Modelization 71 71

72 Market Access Modelization 72 72

73 Value-Based Pricing Across Indications 73 73

74 VBP across Indications Company Perspective 74 74

75 VBP across Indications 75 75

76 VBP across Indications 76 76

77 VBP across Indications 77 77

78 VBP across Indications 78 78

79 VBP across Indications 79 79

80 VBP across Indications 80 80

81 VBP across Indications 81 81

82 VBP across Indications HTA Perspective (Italy) 82 82

83 VBP across Indications 83 83

84 VBP across Indications 84 84

85 VBP across Indications 85 85

86 Conclusion 86 86

87 Conclusion Pharmaceutical Pricing To be understood and implemented early stage Risk Sharing & Performance-Based Agreements A solution for giving access to patients but with administrative and operational burden Use of Big Data & Healthcare Analytics A key trend to follow for better efficacy in care and drug development Changing the Drug Development Paradigm A new paradigm, still to be accepted by regulatory authorities European HTA Collaboration On the right track. It will bring a lot of value even if healthcare budgets are still managed country by country. Health Economics in the Context of Personalized Medicine & Valuing Targeted Therapies Health economics is useful in a pre-determined context to help access Market Access Modelization & Value-based Pricing across Indications Key in order to better anticipate the market reality 87 87