Introductory and advanced course in Health Economic Evaluation

Transcription

1 Introductory and advanced course in Health Economic Evaluation Lieven Annemans Sarajevo, October

2 Outline Coffee Break Lunch Coffee Break Introductory course in Health Economics Part 1 - Repeating the basic principles of health economic evaluation - The most applied method: medical decision trees do s and don ts Practical examples of good and bad evaluations - Budget impact: more than just a simple calculation Advanced course in Health Economics Part 2 - Translating international economic evaluations to the central European context: exercise - The role of health economic evaluations in decision making on price and reimbursement - Integrating health economic evaluation in drug development: challenges and solutions Coffee Break Health economic evaluation of personalized medicine 2

3 1. Repeating the basic principles of health economic evaluation 3

4 What is a Health Economic Evaluation? The comparative analysis of alternative courses of action in terms of BOTH their costs and health consequences Always comparative Always 2 dimensions 4

5 What is Health Technology Assessment? HTA seeks to inform health policy makers by using the best scientific evidence on the medical, social, economic and ethical implications of investments in health care HTA includes: 1.Synthesising health research findings about the effectiveness of different health interventions 2.Evaluating the economic implications and analysing cost and cost effectiveness 3.Appraising social and ethical implications of the diffusion and use of health technologies as well as their organisational implications 4.Helping to identify best practices in health care, thereby enhancing safety, improving quality and saving costs. 5

6 Note: what is meant by technology? Technology is broadly defined to include the drugs, devices, medical and surgical procedures used in health care, as well as measures for prevention and rehabilitation of disease, and the organisational and support systems in which health care is provided 6

7 The Different Steps of Evidence Can it work? Does it work in a controlled environment (clinical trial)= Efficacy e.g. Antihypertensive drug reduced blood pressure more than placebo in a 12 week RCT Does it work in those patients where it is meant to work in and on relevant parameters compared with current care? = Effectiveness e.g. Antihypertensive drug reduces the incidence of heart failure compared to an active comparator in patients with decreased ejection fraction. Is it worth doing it, compared to other things we could do with the same money? = Cost-effectiveness = Efficiency 7

8 e.g. New drug vs. current care (health care payer perspective) New Current Current Net Savings New New Current Cost of drug + = Average Other Treatment Costs physicians hospital surgery other drugs tests Total Cost EXAMPLES: seasonal flu vaccine in chronic elderly prevention of renal disease in 8 high risk diabetics

9 e.g. New drug vs. current care (2) There are savings, but not enough to compensate for the cost of the intervention New Current New Current New Net Costs Current Cost of drug + = Average Other Treatment Costs physicians hospital surgery other drugs tests Total Cost EXAMPLES: Cholesterol lowering drugs in high risk primary prevention 9

10 e.g. New drug vs. current care (3) Because life is prolonged, the costs of other care are increased. New Current New Net Costs Current New Current Cost of drug + = Average Other Treatment Costs physicians hospital surgery other drugs tests Total Cost EXAMPLES: chemotherapy in metastatic cancer 10

11 But remember the definition The comparative analysis of alternative courses of action in terms of BOTH their costs and health consequences 11

12 How to express health effects? yes, our drug is more expensive, but it has: A lower toxicity rate More patients with response to treatment A prolonged duration of response An increased survival An increased quality of life So what? e.g. Extra cost for 100 patients = 100,000 Extra health effect = 10 more responders 10,000 per extra responder so what Need to find an appropriate effectiveness measure for decision making 12

13 QALY = Quality Adjusted Life INDEX ( utility level ) Years Perfect health 1 0.6* Death TIME 13

14 QALY: more realistic HEALTH INDEX ( utility weight ) 1 Drug B No Drug 0 Drug A death TIME Adding Years to Life or Life to Years? 14

15 Example 1: EQ-5D in depressed patients in primary care Utility scores by disease severity over time 0.9 Disease severity 0.8 Mild Health utility Moderate Severe Visits 15 Sobocki P, et al. Value Health Mar-Apr;10(2):

16 Example 2: influenza Luce et al, Vaccine 26 (2008)

17 Exercise: calculate the gain in QALYs years 17

18 Combining the two dimensions Total cost - Current Care C New - Willingness to pay threshold +/- + New E New New Health Effect (QALY) QALY = Quality Adjusted Life Years 18

19 Incremental Cost-Effectiveness Ratio algebraic ICER** = ICER = C new C old* Eff new Eff old C new C old QALY new QALY old * Note that in this cost difference all costs are included (also the possible savings with the new drug) ** Many authors also use here the term ICUR; U = utility 19

20 PROBLEM: where is the threshold? Three approaches: HISTORICAL benchmark 50,000$ per QALY: = cost-effectiveness of caring for a dialysis patient in the USA CONTEMPORARY = The cost-effectiveness of drug X that was reimbursed last year in our country was 6,000 per QALY; ours is only 5,000 per QALY. WHO: 1 to 3 times GDP per capita (based on DALY!) (e.g. Belgium = 30000) 20

21 3 theoretical examples Total cost Current Care New - Societal willingness to pay threshold New 3 + Health Effect (QALY) / 0.5 = per QALY too much! 21

22 A real example in the UK: bevacizumab in metastatic CRC Difference in cost / difference in QALY UK HTA report, 2007, Tappenden et al,

23 ICER of Statins: large differences depending on the study/setting STUDY drug DRUG COST NET COST QALY COST PER QALY CARDS ATORVA dominant LIPS FLUVA S SIMVA IDEAL ATORVA vs SIMVA Lindgren and Jönsson, EJCPR 2009, 16:

24 Treatment Other examples: league table Cost per QALY gained ($) Smoking cessation physician counseling blocker post-mi, high-risk 3,600 Statins (4S) 5,400 AIDS drug cocktails 15,000-20,000 -blocker post-mi, low-risk patient 20,200 Driver-side airbag 27,000 Kidney dialysis 50,000 Annual mammography for women aged ,000 Exercise ECG for asymptomatic man aged 40 years 124,000 Annual CT scan of former heavy smokers to detect lung cancer 1,300,000 Sources: J Probstfield. Am J Cardiol 2003 Johannesson et al. N Engl J Med 1997 Tengs et al. Risk Anal 1995

25 2. Health Economic Modelling 25

26 a. Back of the envelope example 1 Cost A = 1000 Cost B = 2000 Cost of disease X = Suppose 10 patients 7 x x per patient NNT = 5 strategy A Success Prevent disease X strategy B 3000 NNT = number needed to treat Disease Success Disease

27 Back of the envelope example 2 Cost A = 1000 Cost B = 5000 Cost of disease = NNT = 10 strategy A Success Prevent disease X 3000 Disease strategy B Success Disease

28 Back of the envelope example 3 Cost A = 1000 Cost B = 5000 Cost of disease = Prevent disease X strategy A 3000 strategy B 6000 If disease: -2 QALYs Success Disease Success Disease ICER = 3000/0.2 = /QALY gained Incremental cost-effectiveness ratio QALY QALY QALY QALY 28

29 b. Decision trees same concept Prosser et al, Pharmacoeconomics

30 Input data Prosser model 30

31 ARM no vaccination a b a x b COST probability expected No influenza No vaccination Influenza non hosp Cost of vaccine 0 Influenza hosp Influenza death TOTAL 748 US $ 31

32 Vaccination ARM vaccination a b c d = a*b e = c*d No influenza Influenza non hosp no adverse events Influenza hosp Influenza death Cost of vaccine 20 No influenza Influenza non hosp inj side reaction 324 US $ Influenza hosp Influenza death No influenza Influenza non hosp systemic reaction Influenza hosp Influenza death No influenza Influenza non hosp anaphylaxis Influenza hosp Influenza death No influenza Influenza non hosp Guillain-Barré Influenza hosp Influenza death TOTAL 304 US $

33 c. Markov models Purpose: For diseases in which risks are continuous over time and timing of events is important Principles: Disease specific health states Time of evaluation is divided into periods (cycles) At each point in time each patient is in one of the health states During each cycle the patient can move from one health state to another (= transition) transition probability 33

34 Markov Model simple example 3 health states (dead, sick, alive) healthy 0.1 sick dead Upon start after 1 year after 2 years after 3 years Healthy Sick Dead Total

35 Changing the model Suppose a prevention that reduces the incidence of becoming sick by 50% (RRR), based on a one year trial Probability (healthy sick): before = 0.1 ; now = 0.05 per year Upon start after 1 year after 2 years after 3 years Healthy Sick Dead Total The model predicts a lower mortality after 3 years, although the trial was not powered nor sufficiently long to show an impact on mortality. Necessary condition: the link between sick and dead (0.2 per year) needs to be a robust, validated link 35

36 How to calculate a Markov model? Like a decison tree (but repeated) Upon start after 1 year after 2 years after 3 years Healthy Sick Dead Total e.g. Cost Healthy = 200; cost Sick = 3000 yr 1 = 0.89* *3000 = 478 Upon start after 1 year after 2 years after 3 years Healthy Sick Dead Total e.g. Cost Healthy = 200; cost Sick = 3000 yr 1 = 0.94* *3000 =

37 Example: hpv vaccine NoHPVonc HPVonc CIN1onc det CIN23 det Persistent CIN23 det Cancer Cancer cured Death cancer Death det: subjects with disease detected through screening: same pathways but different probabilities Demarteau et al

38 Markov Output : HPV prevalence HPV yearly prevalence per 100,000 subjects HPV yearly prevalance 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, Age Prediction for Population non vaccinated (only screening strategy) Prediction for Population vaccinated (vaccination + later screening) 38

39 Markov Output : cervical cancer incidence Yearly incident cases of cervical cancer per 100,000 subjects Yearly incident cases of cervical cancer Age The model prediction of the current world can be compared with real observed data = outcomes validation Prediction for Population non vaccinated (only screening strategy) Prediction for Population vaccinated (vaccination + later screening) 39

40 How to make models credible and reliable? 1. Validation of structure: does the structure reflect real life? clinical experts 2. Validation of calculations: peer researchers 3. Validation of outcomes: compare outcomes predicted by the current arm with real observations cfr. HPV model 4. Show various sensitivity analyses 40

41 EXAMPLE SENSITIVITY ANALYSIS: Tornado diagram Prosser NOTE: Tornado in the overall population, not elderly 41

42 Probabilistic sensitivity analysis: simple example Available data: Purchase cost of no prevention = 0 Purchase cost of prevention = 4000 Cost of a heart attack = Chance of a heart attack without prevention = 30% Chance of a heart attack with prevention = 20% QALYs without a heart attack = 10 QALYs with a heart attack = 6 No MI No prevention QALY At risk for CHD QALY MI QALY No MI Prevention QALY CHD = coronary heart disease QALY MI QALY 42

43 Prob distribution of inputs Suppose the cost of prevention is not 4,000 as was originally the case, but a normal distribution with average = 4,000 and standard deviation = 500. The cost of a heart attack is not 10,000, but a normal distribution with average = 10,000 and standard deviation = 1,000. The number of QALYs if there is no heart attack is not 10, but a normal distribution with an average of 10 and a standard deviation of 1. The number of QALYs in case of a heart attack is not 6, but a normal distribution with an average of 6 and a standard deviation of 1. Computer runs the model 500 times. For every new calculation, the computer takes a value chosen at random from the respective probability distributions. When the model is calculated for the first time, for example, the computer might use: Cost of prevention = 3,790 (and not 4,000) Cost of heart attack = 8,530 (and not 10,000) Number of QALYs without MI = 10.5 (and not 10) Number of QALYs with MI = 6.2 (and not 6). With these inputs, the model leads to an ICER of 6,712 per QALY (check!) 43

44 Probabilistic sensitivity analysis Costs in Euro QALYs 44

45 Example: ATAC study The analysis showed a probability of 0.98 to be cost-effective, if an acceptability threshold of 30,000 /LY is applied (98% of cases have an ICER below 30,000 /LY) 50,000 Threshold of 30,000 /LY 40,000 Incremental cost 30,000 20,000 10, ,000 Incremental LY Moeremans et al, 2005 (ISPOR) 45

46 Cost-effectiveness acceptability curve make the threshold flexible (in X axis) Probabiliity ,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 Cost-effectiveness threshold Moeremans et al, 2005 (ISPOR) 46

47 In conclusion: which model when? All models are wrong, but some are useful (B. Jönsson) Different models have different strengths and weaknesses. The task of the modeler is to find the best type of model to match the characteristics of the system to be represented and the problem to be solved (D. Eddy) 47

48 In conclusion: what makes a model useful 1. List all assumptions clearly 2. Extensive Sensitivity analysis 3. Write separate chapter describing all validation actions 4. Input data refer to a same event as in the model Don t: model predicts prevention of coronary heart disease; cost of event = cost mycocard infarction (MI CHD!) 5. Input data refer to same population Don t : model is in diabetes patients, but cost of MI is obtained from obese diabetes patients Don t keep transition probailities constant when they are age dependent

49 Budget Impact Analysis more than a back of the envelop calculation 49

50 ICER? = Information Created to Evade Reality Birch and Gafni (2006) We need to answer another question as well There is a need for economic evaluations to address the issue on how to allocate resources efficiently, and for budget impact studies to address the issue of affordability Sullivan et al,

51 Budget impact analysis (BIA) and cost-effectiveness What is totally or partly identical: Underlying clinical assumptions Drug B leads to 20% less complications budget savings Drug B has a 15% better response rate less second line treatment - budget savings. data sources RCT,. Model structure Decision tree, Markov model, 51

52 BIA and cost-effectiveness What is totally or partly different: Perspective = the budget holder Shorter time horizon Comparator = mix of current treatments A BIA works with an open cohort Year 2+, new patients become eligible Model structure «2 layer model» In addition, other data needed (see next slide) 52

53 Extra data needed for budget impact analysis (BIA) Number of eligible patients potential target population Incidence and/or prevalence More specific indication (subgroups) Current market shares in potential target population E.g. drug A 50%, drug B 30%, drug C 20% Extent of replacing current therapies (forecasted market shares) E.g. New drug X will take 10% from A, and 5% from B 53

54 Source for such market penetration data? Geographical benchmark The drug was launched last year in Slovakia and achieved a market share of 10% Historical benchmark The latest launch in depression obtained a market share of 8% after 1 year Market research Presenting new drug profile X to clinicians, and ask about potential for replacing current drugs. 54

55 «2 layer model»: ATAC example (Moeremans et al) step 1: incremental cost of new drug per year for one single patient (here based on Markov) Incremental cost per patient per year Costs Years Launched half-way the year 55

56 Step 2: for each annual cohort, the incremental cost per year can be calculated (= maximal budget impact) Incremental cost / patient x number of patients who start on drug in year 1 (here 3073 patients) Incremental cost per starting cohort per year 3,000,000 2,500,000 2,000,000 Costs 1,500,000 1,000, , ,000-1,000, Years 56

57 Step 3 ( 2nd layer ) : Adding the costs for each new annual cohort 18,000,000 16,000,000 14,000,000 Max budget (yr 6) =16.35Mio 12,000,000 10,000,000 Costs ( ) 8,000,000 6,000,000 4,000,000 Cumulation of population costs per starting cohort (year of diagnosis) (all eligible pts) 2,000,000 = same curve as previous slide 0-2,000,000 Years 57

58 58

59 Remaining issues Where is the extra money to come from? (perhaps the submitter can make proposals) The cost of making a good budget impact model What is an acceptable budget impact?... 59

60 The criteria for adopting a drug Reimbursing a drug with a claimed price premium is generally based on 5 criteria a) Added therapeutical value (relative effectiveness) b) Safety and tolerance c) Cost-effectiveness d) Budget impact e) Medical and therapeutical need 60

61 Exercise: what do we need to translate the following paper to the local setting?

62 3. Use of Health Economic Evaluations and Trends 62

63 Mandatory use in more and more countries Requirements in the approval process mandatory optional upcoming no 63

64 The Fourth Hurdle 64

65 Example: Belgium Class I drug = innovative drugs PE evaluation mandatory Start January Dossier to CRM (Commission for Reimbursement of Medicines) Supported by 8 full time experts: 60 days evaluation time Evaluation to company: 20 days to react Judgement by CRM within 70 days Minister has 30 days to decide 65

66 CTG / CRM Evaluation criteria Therapeutic value Price and proposed reimbursement level Place of the drug in medical practice (therapeutic and social needs) Efficiency (cost for the health insurance / therapeutic value) VALUE FOR MONEY Budgetary impact 66

67 Situation in Europe Huge variety Several decision makers Central level Regional level Hospital level Differences in applied criteria Cost per QALY (NICE) Cost, nothing but cost. 67

68 NICE Decisions Decision Number Percent Unrestricted 98 29% Optimised % Only in Research 21 6% Not recommended 31 9% Non-submission 4 1% Total % 342 individual recommendations in 166 technology appraisals A. Haycox, 2011

69 SMC Results Accepted for Use 34% Accepted for Restricted Use 37% Not Recommended 28% Rule of thirds with little change over time % Accept Restrict No

70 Problem 1. Added therapeutical value 70

71 From absolute efficacy to relative effectiveness Efficacy Effectiveness Relative Versus best alternative Ideal world Real world Real world trial trial no trial RCT vs. best alternative - clinical outcome Relative effectiveness Versus any alternative RCT vs. Any alternative - surrogate marker outcome Post marketing study with comparator Versus placebo RCT vs. placebo Absolute No comparator Absolute efficacy Medical claims data

72 Therapeutical value problems Often need to bridge from efficacy to effectiveness Clinical trials are often of insufficient duration to enable effectiveness evidence Patient selection is not corresponding to real life population Comparator in phase III studies (sometimes placebo!) is not the same as relevant comparator in a country Modelling often required to predict relative effectiveness But are models accepted? 72

73 Examples It has been noted that the study arm populations had, where recorded, median/mean ages of between 5 and 10 years younger than the UK population of people with CRC. (UK HTA report on bevacizumab and cetuximab, 2007) In real life no venographies take place (dossier oral anticoagulant in prevention of DVT). There is insufficient evidence that reduced postprandial glucose leads to less macrovasular and microvascular complications... 73

74 Problem 2: More decision makers, EUnetHTA more networking Regional /hospital power (efficiency affordability? usage?) (private) insurance power... Differentiating messages complexity The good news: Decision makers start to network 74

75 Different Payers, different questions I don t trust your model I want a meta-analysis, a cost per QALY estimate and a probabilistic analysis What is the cost per (cured) patient? What discount can you give me? What is the impact on my budget for pharma? Where am I? How much can you save elsewhere? 75

76 International reference pricing Major threat to more equal patient access 76 Kanavos 2008

77 European, national, regional, and local HTA How much HTA is needed, how much is affordable? 77

78 Proposal to change roles and responsibilities! CURRENT FUTURE Assessment criteria for innovative medicines Centralized Local Integrated Local with exchange EMA HTA and competent bodies Joint Initiative for Medicines (JIM) HTA bodies and competent bodies Efficacy Safety Relative efficacy Relative effectiveness EU medical need Local medical need Ethical and social aspects Cost-effectiveness Budget impact Organizational aspects Belgian EU Presidency report

79 Problem 3: The typical dilemma for The typical Dilemma at Submission proving effectiveness Give us more evidence that your drug is effective and cost-effective PAYER INDUSTRY Allow us first to the market (reimburse the drug) and then we will be able to show real life evidence 79

80 Possible solution for the dilemma Accept models upon submission for reimbursement Short term long term predictions Bridge from efficacy to effectiveness Assessment of robustness Re-evaluation later, based on harder real life evidence coverage upon evidence development (CED) Performance Linked Reimbursement (PLR) 80

81 performance linked reimbursement Velcade UK Sutent/nexavar Italy,... MS drugs UK BUT what about confounding factors? Legal aspects? 81

82 Problem 4: How to measure Utility direct VAS: Visual Analogue Scale SG: Standard Gamble TTO: Time Trade Off indirect EQ 5D SF years in X 8 years in perfect health (= 1 ) 82

83 Example of scenario I. 10 years in X II. OR? OR? 8 years in perfect health (= 1 ) 10 years in X 6 years in perfect health (= 1 ) III. 10 years in X OR? 7 years in perfect health (= 1 ) = X = 0.7

84 Indirect method: EuroQol 5D (EQ 5D) Mobility 1. I have no problems in walking about x 2. I have some problems in walking about 3. I am confined to bed Self-Care 1. I have no problems with self-care x 2. I have some problems washing or dressing myself 3. I am unable to wash or dress myself Usual Activities (e.g. work, study, housework, family or leisure activities) 1. I have no problems with performing my usual activities 2. I have some problems with performing my usual activities x 3. I am unable to perform my usual activities Pain/Discomfort 1. I have no pain or discomfort 2. I have moderate pain or discomfort x 3. I have extreme pain or discomfort Anxiety/Depression 1. I am not anxious or depressed 2. I am moderately anxious or depressed 3. I am extremely anxious or depressed x

85 From EQ-5D to utility: e.g. UK Example: State Full Health 1 Constant term (for any dysfunctional state): Mobility (level 1) -0 Self-care (level 1) -0 Usual activities (level 2) Pain/discomfort (level 2) Anxiety/depression (level3) *2 Predicted TTO value for Coefficients were obtained by multivariate regression analysis linking the TTO outcome with the EQ 5D dimensions: TTO = constant + b1* Mobility +b2* Self-Care +... b5* Anxiety/Depression 85

86 Transformation from EQ 5D applied in Belgium status index status index status index status index status index

87 Is a QALY a QALY? E. Nord, person trade off method

88 Is a QALY a QALY? Plus number, own responsibility, mortality? E. Nord, person trade off method 88

89 UK decisions in function of cost-effectiveness Probability of rejection 1 B 25,000-35, A 0 5,000-15,000 Log [cost/qaly] Rawlins and Culyer, 2004

90 PBAC (Australia) (n = 103) Harris et al. J Med Dec Mak

91 Incremental Cost/QALY ( ) 25,000 e.g. VIAGRA: what is the social/therapeutic need? Not Fully Funded 20,000 15,000 appropriate for NHS funding < 25,000 10,000 5,000 3,369 3,017 2,803 2,695 2,329 0 Year 1 Year 2 Year 3 Year 4 Year 5 Time Horizon 91 Source: Stolk et al, BMJ 2000:320

92 Social reference point (Scitovsky) Health status maximal minimal Striving above SRP Pleasure seeking Not necessary No funding Striving towards SRP Necessity depends on severity Accept higher cost/qaly in worst conditions Societal reference point -Age - Socio-economic - QALY history 92

93 PLUS: Current QALY approach is not sufficiently patient oriented Universal concepts are often omitted from generic health utility measures (EQ5D, SF36) Sleep adequacy Impact of disease and treatment on appearance Sexual functioning Impaired memory/cognition Threats to dignity Fear/concern of worsening disease See Stolk et al. Validity and feasibility of the use of condition-specific outcome measures in health economic evaluations. Quality of Life research 12:

94 Cfr. Who is really interested in cost per QALY? 94

95 Within disease comparisons: IQWIG efficiency frontier

96 Problem 5: need for Indirect and mixed treatment comparisons (ITC ITC & MTC) MTC use meta-analyses to initially estimate dbc and dac and use these estimates to calculate dab (with associated uncertainty)!! Since an indirect comparison requires two meta-analyses to be correct, rather than one, there is, arguably, more scope for error (Sutton, 2008) 96

97 Be aware of population differences Signorovitch et al, 2010

98 Problem 6: Time horizon Guidelines say: the time horizon should be chosen in order to capture all relevant cost and outcomes E.g. model in prevention of CHD (cited in Gold et al): Costs and effects were estimated over a period of 50 years. This time horizon was chosen in order to capture the full lifespan of the youngest cohort in the analysis In practice much shorter: a time horizon of 3 years meets better our needs (decision maker) 98

99 Example Moeremans et al, 2005 (ISPOR) anastrozole in breast cancer outcome of Markov Incremental cost ( ) 6,000 5,000 4,000 3,000 2,000 1,000 Incremental cost Incremental LY Incremental effect (LY) Years

100 Results differ in function of time horizon Incremental cost-effectiveness ratio 47,040 16,885 9, Time horizon (Years) 100

101 Taxonomy Outcomes-based Reimbursement Schemes Unit of analysis Patient Patient population (or sub-population) Outcome measure Response dependent Response assumed (Cost)- effectiveness Maximum treatment cost/patient Terms of settlement Failure cash refund Failure replacement stock Failure pay for consequence Longer treatment required, drug free of charge Higher dose required, drug free of charge Pre-agreed price increase Pre-agreed price decrease +/- rebate Price adjusted +/- cash transfer variable determined by data Examples Velcade Multiple Myeloma Erbitux Colorectal Cancer Actonel, Osteoporosis Lucentis, AMD Revlimid Multiple Myeloma Rimonabant Obesity? Avonex Betaferon Rebif Copaxone Multiple Sclerosis Scheme Janument /Januvia, Diabetes

102 4. Health economics during product development 102

103 The ideal process in pharma Preclinical Early Models STOP 1 Value Proposition Phase I STOP 2b Models Cost of Illness studies QoL & Utility measurement Phase II STOP Product Life Cycle 2 Prepare Value dossier Phase III 3 Value - dossier Launch Date Piggy back (?) Real life data post marketing Reimbursement Models 103

104 4.1. Early models: simple example disease X Treatment A Treatment B Key questions in this example: success 0.80 failure 0.20 success 0.90 failure 0.10 What is the incidence of failure in real life? What is the health burden of failure/disease? How is failure managed in real life what is the cost of failure? 104

105 Early Health economic modelling WHEN: Pre-clinical phase II WHY? Majority of development costs in late development (phase III) Purposes of early models input into go/nogo and priority setting decisions guide the decision on further development, in specific indications/patient groups, (e.g. 2nd line diabetes) to be positioned in comparison with specific competitors, (e.g. metformin + SU vs metformin +NEW) with a given time horizon, (e.g. more than 1 year needed to show sustainable effect on HbA1c) focussing on a given parameter (e.g., the management of a given adverse event, the level of compliance), (e.g. avoiding hospitalized exarverbations) and with an optimal sample size 105

106 Issues with early data collection Data collection takes place before actual product profile is known often lost efforts Does the description of an event in real life match the description of the event in the trials e.g. a sepsis has not the same definition in the model compared to the definition in a trial and the definition in a hospital Risk for contextual or interviewer bias leading to overestimate burden of disease Need for local data collection 106

107 Personalized medicine: a solution to reduce budget impact? 107

108 On first sight, benefits will accrue to Patients all Reduced uncertainty, improved care and less exposure to ineffective treatments Physicians More effective options and outcomes for patients Regulators Increased efficacy and safety Payers & policy makers More cost-effective use of our healthcare Euros Industry Innovative products that offer a clear improvement for patients Based on Finley Austin, ISPOR 2008

109 Economic hurdles! Fragmentation of demand, lower expected revenues reduced R&D incentives? Pricing/reimbursement levels for diagnostic tests and for performing them Budget impact? (including cost of test) Economic consequences of false positive / false negative tests New economic paradigm Based on A. Towse, ISPOR 2008

110 New paradigm? Before Drug A Now no test A? B? disease X disease X No treatment? Drug B Test(s) A! B! Even a test with decent performance may yet provide insufficient value to decision makers (Shih & Pusztai, PhEcon, 2006) No treatment! Cost of test Performance of test Other budget for test, other decision maker Cost of inadequate treatment...

111 Evidence? See Gupta et al, 2008 The somewhat variable and conflicting information available in the literature about EGFR tests suggests that there is a need to develop evidence-based or consensus standardization guidelines for their performance and interpretation of EGFR tests in routine clinical practice (Gupta et al, 2008) The future takes us back to the past: need for evidence! 111

112 Simple example treat all gene expression p_geneexpress no gene expression # response p_response_gene no response response # p_response_no_gene no response metastatic cancer gene expression p_geneexpress # test positive--> treat sens_test test negative--> BSC response p_response_gene no response # test no gene expression # # test positive--> treat test negative--> BSC # response p_response_no_gene no response # spec_test

113 Input data Cost test = 1000 Cost treatment = Cost responder = 8000 Cost non responder = 6000 Cost BSC = 4000 Prob gene expression = 0.4 Prob response if gene expression = 0.8 Prob response if no gene expression = 0.05 Sens test = 0.9 Spec test = 0.6 QALY reponder = 2 QALY no responder/bsc = 1 Treat all vs test: Incr Cost = 2300 Incr Eff = 0.05 ICER = 46000

114 Tornado Tornado Diagram at treat all v. test cost of treatment: to sensititivity of test: 0.8 to 1 QALY if response: 1.5 to 2.5 response if no gene expression: 0.0 to 0.1 probability of gene expression: 0.3 to 0.5 cost of test: 500 to 1500 specificity of test: 0.5 to 0.7 prob of response if expression: 0.7 to K 20K 40K 60K 80K 100K Incremental Cost/Eff

115 4.2. Post Launch real life data a) Collection of data to validate earlier made assumptions (mostly non comparative) = Extended follow up to assess effectiveness and safety 1) Retrospective 2) Prospective b) Real life comparison of technologies (real patients, real treatment setting, ) 1) Retroprojected 2) Prospective 115

116 10 Recommendations for industry 1. Earlier start integrating health economic assessment within development (cfr Buxton: it is always too early, until suddenly, it is too late ) 2. Integrate cost-effectiveness in a cost-effective way (product development teams) align medical, marketing and health economic strategies 3. Increase transparency of cost-effectiveness studies (make models available) 4. Analyze budget impact according to guidelines and show efforts to limit the budgetary impact 5. Pro-active Engagement, Partnership and risk sharing with payers (Ad Boards, Consultancy, Agreements) 6. Show all the value you have got (potential) savings, filling a medical need, gaining QALYs, improving work productivity 7. Promote and foster the cost-effective use of your devices/diagnostics (in the right population, in the right way) 8. Plan post-marketing health economic activities before submission. 9. Internal training (clinical, marketing, ) Cfr. Cost-effective book: health economics for non economists (info@academiapress.be). 116

117 10 Recommendations for payers 1. HTA should be an unbiased and transparent exercise, with possibility for appeal (Drummond et al, 2008) 2. Apply a suffiently long time horizon and a broad perspective when making decisions 3. Follow design-specific guidelines in assessing the quality of submissions 4. Accept price in relation to value (pay for quality) and affordability 5. Finetune the decision (e.g. reimbursed based on patient condition) 6. Be consistent about judging added therapeutical value be explicit about the motivation of the decision 7. Challenge current technologies/drugs, not only new ones 8. Engage with industry agreements (cfr performance based agreements) 9. Invest in infrastructure and training.. 117

118 10.Don t EVER give up!

119 A cost-effective book Health economics for non-economists An introduction to the concepts, methods and pitfalls of health economic evaluations ISBN XIV p. orders@academiapress.be OR Non-Economists-Introduction- Evaluations/dp/ /ref=sr_1_1?ie=UT F8&s=books&qid= &sr=

120 Introductory and advanced course in Health Economic Evaluation Lieven Annemans Sarajevo, October