TECHNICAL APPENDIX 1 THE FUTURE ELDERLY MODEL

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Ernest Robbins
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1 TECHNICAL APPENDIX 1 THE FUTURE ELDERLY MODEL To estimate the potential health benefits of PCSK9 inhibitors, we use the Future Elderly Model (FEM), a dynamic microsimulation model developed by Goldman et al. (2004) that has been used to study a wide variety of health policy questions with support from National Institutes of Aging, the MacArthur Foundation, and the Department of Labor. FEM tracks patients aged 50 years and older and projects their health and economic outcomes. Unlike many other social benefit simulation models, FEM estimates individual health trajectories rather than aggregating health characteristics of a cohort. FEM was developed to forecast the implications of different medical technology interventions on long-term health and estimate the impacts of health policy changes, including estimating the value of PCSK9 inhibitors in hypercholesterolemia patients, 1 the impacts of statins in an obese population, 2 delayed biological aging, 3,4 and delayed Alzheimer s Disease. 5 The model and methods are briefly described here; complete technical information is available in the FEM technical Appendix. The FEM simulates the outcomes of older Americans based on the Health and Retirement Survey (HRS), a nationally representative biennial survey of Americans aged 51 and older which are used to compute the health transition models at the core of FEM. And to characterize the population that goes into the simulations. The FEM has three core modules. The Health Transition module calculates transition probabilities across various health states to model the health of the 51+ population over its lifetime. Health transitions include the presence of certain chronic conditions, functional status, mortality, biomarkers, and respondents current therapies for cholesterol, diabetes, and blood pressure control. All transition probabilities are modeled with first-order Markov processes controlling for age, gender, education, race, body-mass index, smoking behavior, and health at the time of entry into the simulation. Most chronic conditions are treated as absorbing, meaning that once an individual receives an initial diagnosis, they have the condition until death. Two non-absorbing conditions myocardial infarction (heart attack) and re-occurring stroke were modelled to estimate the probabilities of having these conditions in every simulated year. Biomarker values (specifically cholesterol, HbA1c, and blood pressure) were added to the list of covariates predicting cardiovascular diseases, diabetes, and hypertension. We predicted quality-adjusted life-year (QALY) measures using EQ-5D, a standardized and wellvalidated health-related quality-of-life instrument measuring a respondent s general health status based on five dimensions: mobility, daily activities, self-care, anxiety, depression, and pain. 6 The Medical Expenditure Panel Survey (MEPS) data were used to estimate EQ-5D scores with an ordinary least squares regression as a function of a patient s chronic conditions and their FEMspecified functional status. The second FEM component is the Policy Outcomes module, which predicts an individual s health care spending and other economic outcomes (i.e. social security and disability benefits). The FEM first predicts expenditures for beneficiaries enrolled in Medicare and Medicaid, as well as expenditures by private insurers. Medical spending includes the costs of medical provider visits, 1

2 hospital events, inpatient stays, outpatient visits, emergency department visits, dental care, home health care, optometry, other medical equipment and services, prescribed medicines, and nursing home stays. The Medical Expenditure Panel Survey was used to estimate the spending for individuals prior to age 65 and the Medicare Current Beneficiary Survey was used for the spending on individuals aged 65 and older. The estimates are based on pooled least squares regressions of each type of spending on risk factors, self-reported conditions, and functional status, with spending inflated to current dollars using the medical component of the consumer price index. The third model component is a Replenishing Cohort module, which uses data from the Health and Retirement Study to introduce new cohorts of 51-year-olds in each simulated year. The National Health Interview Survey, the Current Population Survey, and the National Health Nutrition and Examination Survey data were used to predict the demographic, social economic status, and the health status for these younger populations. 2 SIMULATIONS We conducted a population-wide simulation to investigate population-wide trends observed in a representative cross-section of the specific U.S. population in each period from 2016 onwards. A full FEM population (including the replenishing cohort) was used to project the outcomes for the entire population of Americans aged 51 years and older, with population health outcomes calculated by aggregating individual health measures in each simulated year. 2.1 SCENARIOS We first created a baseline scenario assuming treatment strategies for managing hypercholesterolemia in the U.S prior to the introduction of PCSK9 inhibitors. Next, we generated three scenarios representing the introduction of PCSK9 inhibitors with differing assumptions about their uptake and efficacy. We reflect the health impact of PCSK9 inhibitors reported in clinical trials by adjusting health transitions and outcomes of eligible individuals accordingly, together with estimates about expected healthcare spending due to PCSK9 inhibitors, and contrast these to the standard of care. We identified the population eligible for PCSK9 inhibitors based on the original FDA-approved indications, as well as the inclusion criteria in the FOURIER trial, which was the first published randomized clinical trial to evaluate the efficacy of PCSK9 inhibitors on hard clinical endpoints pertinent to cardiovascular risk reduction. 7,8 Individuals aged 51 and older with an existing diagnosis of a cardiovascular condition and LDL cholesterol level of at least 70mg/dL while receiving cholesterol-lowering therapy were deemed eligible for PCSK9 inhibitors. Those with familial hypercholesterolemia (defined as LDL cholesterol level higher than 190 mg/dl) with or without a cardiovascular condition were also eligible in our model. We assumed that the eligibility for PCSK9 inhibitors is an absorbing state given that hypercholesterolemia is a chronic condition that requires lifetime treatment to maintain LDL-C level within an acceptable range. We estimated that in 2016, 14 million individuals would be eligible for PCSK9 inhibitors in the U.S. population aged 51 and older. 2

3 Uncertainty surrounding long-term effectiveness and high retail cost concerns has served as barriers to widespread adoption of PCSK9 inhibitors. 9,10 Health care payers commonly respond to such uncertainty by limiting access to the drug using prior authorization, high patient copays and other utilization management tools. As a result, the adoption of PCSK9 inhibitors in the real-world setting has been very slow. 9,11 Therefore, to reflect the uncertainty about the drugs real-world effectiveness and their slow uptake, three scenarios were designed with varying uptake functions and assumptions about the drugs effectiveness: 1) An Expected Efficacy scenario assumes that the real-world effectiveness of PCSK9 inhibitors in cardiovascular outcomes is consistent with the efficacy reported FOURIER. In this scenario, we assumed the number of PCSK9-treated patients begins with 0% of the eligible population in 2014 and gradually rises to a maximum of 5% of the eligible population in This pattern is consistent with the evidence presented in Cannon et al. 2017, who estimate that 14% of the high-risk population would be eligible for PCSK9 inhibitors, with between 31% - 37% of these eventually receiving the therapy. 9,11 The shape of our uptake function is based on Robey S et al. 2017, who find that, on average, biologics take approximately 6 years to reach peak sales, following an S-shape trajectory. 12 Under these assumptions, we estimated the number of treated patients reaches approximately 0.5 million in 2019 and the cumulative number of patients by 2021 is 2.54 million, which is consistent with the estimates reported by the Institute of Clinical and Economic Review. 13 2) A Low Efficacy scenario assumes that PCSK9 inhibitors have real-world effectiveness below that reported in the FOURIER. We assumed the same uptake function between as under Expected Efficacy but decrease the treated population to 2.5% of the eligible patients by the end of the exclusivity period. 3) A High Efficacy scenario assumes that PCSK9 inhibitors have real-world efficacy above that reported in FOURIER, with the proportion of treated patients increasing from 5% in 2025 to 10% by the time the drug loses exclusivity. By the end of the exclusivity period, we expect a cumulative 11.9 million patients would receive PCSK9 inhibitors under the Expected Efficacy scenario, and 9.6 million and 16.3 million patients treated under the Low and High Efficacy scenarios, respectively. 2.2 THE IMPACT OF PCSK9 INHIBITORS ON HEALTH To reflect the health impacts of PCSK9 inhibitors, we modified the transition probabilities of treated individuals in each PCSK9 scenario by reducing the probabilities of myocardial infarction or stroke based on FOURIER results. In the Expected Efficacy scenario, we associate PCSK9 inhibitors with a risk reduction of 0.73 for myocardial infarction (MI) and of 0.79 for stroke. These estimates correspond to the mean estimates reported in FOURIER, with an assumption of a constant hazard ratio over time. 7 In the Low Efficacy scenario, we assume a risk reduction of 0.86 in MI and of 0.90 in stroke. In the High Efficacy scenario, we assume a risk reduction of 0.59 for MI of 0.68 for Stroke. For each simulated year, we estimated the total numbers of incident MI and stroke cases for each uptake and efficacy scenario as well as for the baseline scenario (without the use of PCSK9 inhibitors). Next, we calculated the numbers of events avoided under each scenario compared to the baseline scenario. We estimated a cumulative 340 thousand events would be 3

4 avoided in the Expected Efficacy scenario by the end of exclusivity period; and 160 thousand and 740 thousand events would be avoided in Low and High Efficacy scenarios, respectively. 3 THE PRICE OF PCSK9 INHIBITORS IN THE THREE-PART PRICING MODEL As illustrated in Figure 1, we established a novel Three-Part Pricing (TPP) Model which includes a three-year Evaluation Phase, followed by a seven-year Reward Phase, during which the price will depend on the real-world effectiveness demonstrated in the Evaluation Phase. The price will decrease substantially in the Access Phase to enable broader access to the therapy. In our model, we compared the results of TPP model to a standard fixed-fee-per-dose payment (or FFPD), which assumes a constant rebate rate for PCSK9 inhibitors over the entire exclusivity period. Based on its list price in 2017, the annual per-patient cost of PCSK9 inhibitors would be $14,542, 14 assuming a flat 34% discount off the list price under FFPD. 15 Under TPP, several decisions were made to calibrate the price in each phase. First, we impose a 50% discount off the list price in the Evaluation Phase. In the Reward Phase, to ensure that the price is proportional to the drug s efficacy, the price is calibrated such that the average cost per event avoided is equal across all three efficacy scenarios. In addition, we categorize a drug as having Expected efficacy when the risk reduction ranges from 0.66 to (with a median 0.73) for MI or to (with a median 0.79) for stroke in the treated population. We categorize a drug as having High efficacy when the risk reduction falls below those ranges, and as having Low efficacy when the risk reduction ranges above those ranges. Finally, we varied the price in the Access Phase in the Expected Efficacy scenario to ensure the cumulative cash flow of the drug to the manufacturer is the same under both TPP and FFPD over the exclusivity period if the drug s effectiveness meets expectations. This ensures that the manufacturer will be indifferent between the two pricing models ex ante. We impose a uniform 75% discount to the list price in the Access Phase under all scenarios, which results in the same total cashflow in expected efficacy under both payment models. 4 EXTERNAL VALIDATION We conducted a cohort simulation by disabling the Replenishing Cohorts Module, and thereby followed a representative cohort of Americans aged in 2016 until their deaths. We tracked the effect of the treatment intervention on their lifetime outcomes with and without PCSK9 inhibitors. In the Expected Efficacy scenario, we use the parameters reported in Fonarow et al (including the direct medical costs associated with MI and stroke incidence, and the disutility of injection site reaction due to PCSK9 treatment) to model the average incremental costs per quality-adjusted life years gained per person among those eligible for the treatment. Our cost per event avoided and cost per QALY gained estimates are comparable with Fonarow et al. s results (which reported an incremental $268,637 per QALY gained at the list price of PCSK9 inhibitors) REFERENCES 1 Cheng, W. H., Gaudette, E. & Goldman, D. P. PCSK9 Inhibitors Show Value for Patients and the US Health Care System. Value in health : the journal of the International Society for 4

5 Pharmacoeconomics and Outcomes Research 20, , doi: /j.jval (2017). 2 Gaudette, E., Goldman, D. P., Messali, A. & Sood, N. Do Statins Reduce the Health and Health Care Costs of Obesity? PharmacoEconomics 33, , doi: /s y (2015). 3 Goldman, D. P., Gaudette, E. & Cheng, W. H. Competing Risks: Investing in Sickness Rather Than Health. American journal of preventive medicine 50, S45-50, doi: /j.amepre (2016). 4 Goldman, D. P. et al. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health affairs (Project Hope) 32, , doi: /hlthaff (2013). 5 Zissimopoulos, J., Crimmins, E. & St Clair, P. The Value of Delaying Alzheimer's Disease Onset. Forum for health economics & policy 18, 25-39, doi: /fhep (2014). 6 Dolan, P. & Roberts, J. Modelling valuations for Eq-5d health states: an alternative model using differences in valuations. Medical care 40, (2002). 7 Sabatine, M. S. et al. Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease. The New England journal of medicine 376, , doi: /nejmoa (2017). 8 The FDA Label for REPATHA. (2015). < 9 Navar, A. M. et al. Association of Prior Authorization and Out-of-pocket Costs With Patient Access to PCSK9 Inhibitor Therapy. JAMA cardiology 2, , doi: /jamacardio (2017). 10 Hlatky, M. A. & Kazi, D. S. PCSK9 Inhibitors: Economics and Policy. Journal of the American College of Cardiology 70, , doi: /j.jacc (2017). 5

6 11 M, C. Data Show it s Hard to Fill PCSK9 Prescriptions, Confirming Cardiologists Complaints., < Prescriptions-Confirming-Cardiologists-Complaints.> (2017). 12 Robey, S. & David, F. S. Drug launch curves in the modern era. Nature reviews. Drug discovery 16, 13-14, doi: /nrd (2017). 13 Tice, J. A., Ollendorf, D. A., Cunningham, C., Pearson, S. D. & Kazi, D. S. PCSK9 Inhibitors for Treatment of High Cholesterol: Effectiveness, Value, and Value Based Price Benchmarks. (2015). < Posting pdf>. 14 Kazi, D. S. et al. Updated Cost-effectiveness Analysis of PCSK9 Inhibitors Based on the Results of the FOURIER Trial. Jama 318, , doi: /jama (2017). 15 Global pharmaceuticals industry overview/analysis. (2016). < &serialid=WA0fY0Lhy75fzAbFCzsOZ9eQdrH78BmqbR32eAb6m1s%3D>. 16 Fonarow, G. C. et al. Cost-effectiveness of Evolocumab Therapy for Reducing Cardiovascular Events in Patients With Atherosclerotic Cardiovascular Disease. JAMA cardiology 2, , doi: /jamacardio (2017). 6

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