Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

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Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

Max Korman

Thesis submitted as a part of the Master of Philosophy Degree in Health Economics, Policy and Management

University of Oslo, The Faculty of Medicine,

Department of Health Management and Health Economics

May 2016

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Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

by Max Korman

Supervisor: Torbjørn Wisløff

Thesis submitted as a part of the Master of Philosophy Degree in Health Economics, Policy and Management

University of Oslo, The Faculty of Medicine, Department of Health Management and Health Economics

May 2016

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Copyright Max Korman

2016

Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

Max Korman

http://www.duo.uio.no

Trykk: Reprosentralen, Universitetet i Oslo

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Abstract

Title: Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

Introduction & Background: Cardiovascular disease (CVD) is the biggest cause of premature death and loss of quality of life in the developed world. Despite the success of statins in recent years, there remains unmet clinical need for pharmacological LDL-C reductions to prevent CVD resulting from atherosclerotic buildup. New injectable

monoclonal antibodies (mAbs) called PCSK9 inhibitors reduce LDL-C by 55-65%, but most long-term studies on their preventative effect against CVD have not yet been completed. Two PCSK9 inhibitors, evolocumab and alirocumab, have been approved for use in Norway, but not yet for reimbursement through public national insurance.

Methods: A state transition Markov model was developed to evaluate the cost-effectiveness of PCSK9 inhibitors for the prevention of myocardial infarctions, coronary heart disease death, and ischemic strokes in various high-risk patient subpopulations in Norway. Both evolocumab and alirocumab are compared against each other and against ezetimibe. Baseline risk of CVD is based on population incidence rates and adjusted according to various

baseline risk factors. Preventative effect of treatment was modeled according to absolute reduction in LDL-C achieved. Both primary and secondary prevention settings were analyzed.

Results: PCSK9 inhibitors were never cost-effective in primary prevention, as the

incremental cost per quality adjusted life year was consistently millions of Norwegian kroner.

In secondary prevention PCSK9 inhibitors were cost effective only for very high-risk patients, and only when therapy is initiated at 65 years of age or later. The lowest cost- effectiveness ratios were for heterozygous familial hypercholesterolemia patients and high- risk diabetics, with 565,000 and 611,000 NOK/QALY respectively. Probability of cost- effectiveness generally increased with age until 65 years, at which point it decreased slightly.

Treating men was found to be more cost-effective than treating women of the same age.

Evolocumab extendedly dominated alirocumab in every analysis.

Conclusion: Lifetime costs of PCSK9 inhibitors are too high to be offset by estimated health gains for most eligible patients. Treatment with PCSK9 inhibitors should only be considered in secondary prevention for older patients with very high absolute risk of CVD. Access can be increased to younger high-risk patients as price decreases. Future research is needed to determine the long-term preventative effects of PCSK9 inhibitors.

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Acknowledgements

I would like to thank my supervisor, Torbjørn Wisløff, who seems to know just about

everything about health economic modeling. This would not have worked without his expert guidance and support.

Your input was always greatly valued and deeply appreciated. Thank you.

Also I would like to thank the department, my professors, and my fellow students over the past two years. Without all of them I would not have the foundation and training to have completed this thesis. I am thankful for this experience.

Finally, I would like to thank my wife, Gitte. No one could ever ask for a more loving, supportive, and perfect partner.

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1 Introduction and Background

Cardiovascular disease (CVD) is the biggest cause of premature death and loss of quality of life in the developed world. (1) CVD accounted for roughly 30% of all deaths in Norway in 2014. (2) Atherosclerotic cardiovascular disease (ASCVD)— atherosclerotic buildup resulting in coronary heart disease (CHD), myocardial infarction, and ischemic stroke—

represent the largest, costliest, and most harmful aspects of this CVD burden. (1,3)

Low-density lipoprotein cholesterol (LDL-C) is known colloquially as “bad cholesterol.”

High levels of LDL-C and of total cholesterol (TC) are well known as major risk factors for the development of atherosclerotic cardiovascular disease and the incidence of acute cardiac events—especially myocardial infarction and ischemic stroke. LDL-C and TC are modifiable factors, the lowering of which can lead to decreased risk of CVD; this is evidenced by the success of both lifestyle modifications and statin therapy in reducing the incidence of cardiac events and ASCVD in many populations. (1,3–5)

The success of statins is particularly illustrative of the benefits of pharmacological LDL-C reductions. (4,6,7) Individuals who are at risk for ASCVD events are recommended to be treated with high-intensity LDL-C reduction therapies; target reductions for these patients is to reduce LDL-C to <70mg/dL (<1.81 mmol/L) for those at very high risk, <100mg/dL (<2.59 mmol/L) for those at moderate to high risk, or LDL-C reductions of >50% when absolute targets cannot be met. (1,3) Despite widespread use and success of statins in the reduction of LDL-C and prevention of CVD, however, there remain unmet clinical needs in achieving LDL-C reduction goals. (4,5,8–11)

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an enzyme encoded by the gene of the same name. The primary role of PCSK9 molecules is to bind to LDL receptors (LDL-R) and induce their breakdown. LDL-R is responsible for binding to LDL-C and removing it from the blood, thus reducing the risk of atherosclerotic buildup. (1,3,5) PCSK9 molecules have what is essentially an inverse relationship with LDL-R: the more free-PCSK9 in the blood, the fewer LDL receptors there are. This in turn leads to higher LDL-C levels, as seen in gain-of-function genetic mutations. PCSK9 inhibition functions similarly to a loss-of- function mutation: inhibition of the gene and reduction of free-PCSK9 in the blood means

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fewer LDL-R are broken down. When LDL-R is not broken down by PCSK9, it is able to continually clear LDL-C from the blood. (4,5)

Currently, the most advanced form of PCSK9 inhibition therapy is through injectable monoclonal antibodies (mAbs). (5) Two such drugs have recently been approved for use as lipid modifying therapies in both the US and Europe: evolocumab and alirocumab. Both drugs have been demonstrated to be safe, well tolerated, and extremely effective at lowering concentrations of LDL-C in the blood—in most cases, LDL-C levels are lowered by 55-65%.

(12–14) Cost-effectiveness of these drugs has not yet been well established. (11) One preliminary report out of the US suggests that they are not cost-effective. (10) The drugs are significantly less expensive in Europe, however, which means they may be cost-effective for other countries, including Norway. The UK’s National Institute for Health and Care

Excellence (NICE), for example, initially rejected evolocumab as not cost-effective, but after reevaluation has recently approved it for limited use in some high-risk patient groups. (15)

Current unmet needs in LDL-C reduction reflect the candidate populations who are targets for PCSK9 inhibitors. First are those with heterozygous familial hypercholesterolemia (HeFH), which is estimated to effect anywhere from 1/500 to 1/200 Caucasians. HeFH is a genetic mutation that causes dangerously high LDL-C levels from a young age, leading to a significant increase in the lifetime risk of acute coronary events, with 85% of males and 60%

of females experiencing acute CHD by the age of 60. (5,16) The ideal treatment goal for HeFH patients is to reach an LDL-C level below 70 mg/dL (1.81 mmol/L), but it is estimated that fewer than 20% of these individuals meet this goal with statins alone. (4) Randomized control trials for HeFH patients show promising results of LDL-C reductions with PCSK9 inhibitors. (12–14)

Second are patients with statin intolerance. Due to side effects—most commonly myopathy and muscle pain—some patients are not able to tolerate any statins or can do so only in small doses. Statin intolerance due to adverse effects has been observed in 10-15% of individuals treated with statins. (17) This means individuals with statin intolerance who are at high risk for cardiovascular disease are an important candidate population for PCSK9 inhibition, as there is immediate unmet need for LDL-C reduction. (4,5,10)

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Third, individuals at high risk for CVD who are not meeting target goals for lipid reduction with statin and lifestyle changes alone also have unmet clinical need. Patients who are not meeting lipid reduction goals who have already experienced CVD events in the past are at particularly high risk, as previous CVD is one of the most significant risk factors for future cardiac events. The other most significant risk factors are diabetes, hypertension, smoking, and other lifestyle factors including diet and lack of exercise. (1,3–5)

A lipid-modifying drug called ezetimibe, which reduces the absorption of cholesterol in the intestines, has been used with some success in recent years as a next-line-of-defense

cholesterol treatment. (18,19) PCSK9 inhibitors reduce LDL-C with far greater effectiveness, however, and ezetimibe has certainly not addressed all unmet clinical need. (12–14)

The objective of this thesis is to develop a state-transition Markov model in Microsoft Excel and to evaluate the cost-effectiveness of PCSK9 inhibition for the prevention of

cardiovascular events and ASCVD in Norway.

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2 Methods

2.1 The Decision Problem:

Is the use of PCSK9 inhibitors for the prevention atherosclerotic cardiovascular disease (ASCVD) cost-effective for use in Norway? ASCVD is defined in this analysis as myocardial infarction, death from coronary heart disease, and ischemic stroke. (3) Cost-effectiveness will be determined on the basis of societal willingness-to-pay (WTP) thresholds. (20,21)

2.2 Hypothesis:

Cost-effectiveness of PCSK9 inhibitors will largely be a function of risk. The greater the baseline risk of the patient, and the greater extent the risk reduction as a result of LDL-C reduction therapy, the more cost-effective the treatment will be.

2.3 Patient Populations and Subpopulations:

The model created for this analysis is flexible and capable of accounting for a good deal of heterogeneity. This allows for a number of different analyses to be performed on various modeled patient cohorts. Chosen patient groups reflect individuals at high risk for

cardiovascular disease within the Norwegian population. Specific baseline characteristics of patient cohorts were selected for analysis according to areas of unmet clinical need, expert opinion regarding the most likely contexts of initial use for these drugs, previous analyses of PCSK9 inhibitors, and baseline characteristics of patients in randomized control trials. (10–

14)

Specifically, this analysis will consider both primary and secondary prevention for each of the following patient groups:

1. Diabetic patients with high risk for CVD and persistently high cholesterol, despite maximally tolerated statin therapy

2. Heterozygous familial hypercholesterolemia (HeFH) patients who have not reached LDL-C reduction targets with statins alone

3. Statin intolerant individuals with high risk and high cholesterol levels 4. Other high risk individuals with persistently high cholesterol, despite

maximally tolerated statin therapy

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Cohorts will be stratified by age from the initiation of PCSK9 inhibitor therapy. Some

analyses will stratify cohorts by gender as well. Smoking status and hypertension will also be used as part of baseline risk profiles. Cardiovascular risk factors and baseline patient

characteristics, including cholesterol levels, are selected according to realistic scenarios and clinical need.

2.4 Setting and Location:

The setting of this analysis is the Norwegian healthcare sector, which is publicly financed by the Norwegian National Health Insurance scheme. Allocation of national insurance resources to treatments and patient groups within the Norwegian healthcare sector is decided, in part, on the basis of economic evaluation. Specifically, health-technology assessments (HTA) and cost-effectiveness analyses (CEA) are undertaken as part of the allocation decision process for new drugs. (22)

2.5 Perspective:

The health care payer perspective is chosen for this analysis and as such the focus is on direct medical costs. (20,21) Direct costs included are all those associated with treating

cardiovascular events, costs of health outcomes and post-CVD health states, and comparator treatment costs. Costs reflect all those that are relevant to the Norwegian national insurance scheme with regards to the treatment of myocardial infarction, CHD death, and ischemic stroke. While societal costs in a broad sense are excluded from this analysis, production loss was included as scenario analysis.

2.6 Comparators:

It is important to note that PCSK9 inhibitors are not a first-line-of-defense in CVD risk reduction. Lifestyle considerations including diet, smoking cessation, and exercise are

fundamental to CVD prevention. (1,3) Statins are considered the first-line-of-defense in terms of pharmacological treatments, and have been established as cost-effective and successful for LDL-C reductions and prevention of cardiac events. (1,3,6,7,12,13) Currently, ezetimibe is used in some cases as a next-line-of-defense pharmacological intervention to address unmet clinical need for LDL-C reduction; it is much cheaper than PCSK9 inhibitors, though significantly less effective at reducing LDL-C. Ezetimibe has been used for several years as

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both a monotherapy and in combination with statins, and has recently been proven to reduce the risk of CVD. (18,19) PCSK9 inhibitors are also considered a next-line-of-defense in patients for whom statins are not effective enough or for patients who cannot take statins.

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The PCSK9 inhibitors evolocumab and alirocumab are the two interventions under

consideration in this analysis. Both of these drugs are approved for use in Norway in terms of safety and efficacy, but not yet for reimbursement through national health insurance;

currently, these are the only two PCSK9 inhibitors available in any healthcare market. (12–

14) Ezetimibe will also be a comparator; it has been cited previously as a relevant comparator in this context and an analysis of PCSK9 inhibitors that did not include all next-line-of- defense drugs would be incomplete. (10,11) Both PCSK9 inhibitors will be compared against each other and against ezetimibe. Analyses will be stratified according to the appropriate adjunct statin level, per recommended use for both PCSK9 inhibitors and ezetimibe. (10,12–

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Statins alone are not really a relevant comparator, as PCSK9 inhibitors are not being

considered as an alternative to statins. Nevertheless, a standard/do-nothing option is included in the analysis. This is assumed to be whatever statin regimen the patient is on before

consideration of next-line-of-defense pharmacological intervention.

2.7 Time Horizon:

A lifetime horizon was chosen for this analysis. While LDL-C reductions from these drugs are evident in as little as several weeks, prevention of cardiac events is only clear over many years of consideration. (6,7) Prevention of CVD risk and LDL-C reduction is a question of long-term risk reduction. Initiation of PCSK9 inhibitor therapy, as is the case with statin and/or ezetimibe therapy, is typically seen as the initiation of lifelong treatment. The benefit of LDL-C reduction in younger, high-risk patients is primarily evident as a reduced CVD incidence later in life, such as a reduced 10- or 20-year risk. (11)

It is also important to consider the cost of PCSK9 inhibitors from a long-term perspective; the cost of treatment must be considered from a lifelong perspective in order to accurately

capture the true costs incurred by and reduced by these drugs.

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2.8 Discount Rate:

A discount rate of 4% is used for both future costs and utilities, as suggested by the Norwegian Ministry of Finance. (22)

2.9 Health Outcomes:

The primary health outcome of this analysis is the Quality-Adjusted Life Year (QALY). The model is also equipped to estimate life years (LY) gained, reductions in first-ever CVD events, and reductions in overall cardiovascular deaths.

2.10 Cost-Effectiveness Outcomes:

Cost-effectiveness results are presented as the incremental cost per unit of effectiveness, known as the incremental cost-effectiveness ratio (ICER). (20,21,23) For the comparison between a PCSK9 inhibitor and ezetimibe, the ICER would be calculated as:

𝐼𝐶𝐸𝑅 = 𝐶𝑜𝑠𝑡!"#$! !"!!"!#$% − 𝐶𝑜𝑠𝑡!"!#$%$&!

𝑄𝐴𝐿𝑌!"#$! !"!!"!#$% − 𝑄𝐴𝐿𝑌!"!#$%$&! = ∆𝐶𝑜𝑠𝑡𝑠

∆𝑄𝐴𝐿𝑌𝑠

2.10.1 Borderline Cost-Effectiveness:

Though it is widely used in Norwegian economic evaluations, the 600,000 NOK willingness- to-pay (WTP) threshold per additional QALY gained is an unofficial guideline rather than a strict rule. Treatments and drugs with ICERs higher than this are often approved for

reimbursement in Norway. (24) Therefore, this report will consider treatments with ICERs between 600,000 and 700,000 NOK/QALY to be borderline cost-effective and report them as such. A recent review of decisions made by the Norwegian Medicines Agency confirms that a Norwegian threshold for drugs is likely in the range of 600,000 and 700,000 NOK/QALY.

(25) Holding to a strict 600,000 NOK/QALY threshold without indicating borderline ICERs would be misleading given the real-world leniency for when an ICER is considered

acceptably cost-effective in the Norwegian healthcare system.

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2.11 Measurement of Effectiveness:

Because PCSK9 inhibitors treat biomarkers and intermediaries—chiefly LDL-C—rather than treating a specific disease or acute event directly, the effect of PCSK9 inhibitors on the prevention of cardiovascular disease was modeled through the estimated effect of a reduction in LDL-C.

Given the fact that PCSK9 inhibitors were only recently developed and approved for patient use worldwide, there is a dearth of information regarding prevention of cardiovascular

disease as a direct result of PCSK9 inhibition therapy. Currently results are only available for a handful of long-term studies with appropriate outcome measures; most RCTs have less than 6 months of follow-up. (12–14) Results of long-term RCTs for the prevention of CVD with both evolocumab and alirocumab are expected in 2017. (10) What is known with more certainty, in the meantime, is the effect PCSK9 inhibitors have in terms of LDL-C reduction, with most patients experiencing 55-65% LDL-C reduction in as little as a few weeks.

Relative risk reductions on hard clinical endpoints as a direct result of using PCSK9

inhibitors have been calculated in meta-analyses, but these estimations are based on limited data and have extremely wide confidence intervals. (12–14)

Multiple meta-analyses performed by the Cholesterol Treatment Trialists’ (CTT)

Collaboration, which include hundreds of thousands of patients studied over many years, provide estimates for the relative risk reduction of CVD events per unit of LDL-C reduction achieved on statin therapy. These studies indicate that reduction in risk is proportional to reduction in LDL-C (Table 1). This observation has been demonstrated to hold across all baseline levels of risk and therapeutic intensities of statins studied: extent of risk reduction scales with the absolute level of LDL-C reduction, seemingly without any upper or lower limit. (6,7) The preventative effect of ezetimibe combined with statin therapy is also

consistent with the CTT results. (6,7,18) Modeling the effect of PCSK9 inhibitors through the relative risk estimates of these meta-analyses has been suggested as the best solution until more data becomes available. (10,11) Preliminary estimates for the preventative effect of PCSK9 inhibitors are consistent with CTT analyses; see Appendix 1. Some extrapolation was required, as CTT meta-analyses were based on studies with follow-up times of roughly five years. The relative risk reductions were quite constant and consistent over five-year periods, so it was assumed that they would remain so over all subsequent years. No data is available

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to suggest any potential change in the relationship between LDL-C and CVD after five years.

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Table 1. Key Treatment Effect Parameters

Relative Risks (per mmol/L LDL-C reduction) RR (SE) Source

Nonfatal MI 0.74 (0.03) (6,7)

CHD Death 0.81 (0.01) (6,7)

Nonfatal IS 0.81 (0.05) (6,7)

Fatal IS 0.91 (0.10) (6,7)

Other CVD Death 0.95 (0.10) (6,7)

% LDL-C Reductions from Baseline % Change (SE) Source

Evolocumab 0.63 (0.01) (13)

Alirocumab 0.56 (0.01) (13)

Ezetimibe 0.24 (0.01) (13)

Table 1. Mean relative risk (standard error) of CVD after lipid-modifying treatment per mmol/L of LDL-C reduced from baseline; and mean percentage reduction in LDL-C (standard error) from PCSK9 inhibitors.

2.12 Measurement and Valuation of Preference-Based Outcomes:

The primary utility measure of the model is the Quality Adjusted Life Year (QALY). QALYs combine length and health-related quality of life (HRQoL) into a single generalized measure, which allows for comparisons of effectiveness both within and across various treatment types. (20,21,23) This report combines QALY estimates from various articles and analyses, each of which uses the EQ-5D HRQoL questionnaire and Time-Trade-Off (TTO) methods.

QALY values are assigned to all chronic CVD states as well as healthy, non-CVD states (Table 2). All QALYs used in this model are calculated from EQ-5D results according to the commonly used UK index tariff; use of the same tariff for all values helps to maintain

consistency across QALY estimates. (26,27) Multiple studies have demonstrated that the EQ- 5D is valid for use in various acute cardiovascular events—meaning it is sensitive to and captures HRQoL changes resulting from both nonfatal MI and nonfatal IS. (23,28–31) QALY loss as a result of manifest ASCVD is implemented into the model in the form of a

proportional decrement. More details on parameters and how utilities are modeled are given in Appendix 1.

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Table 2. Key Utility Parameters

CVD Utilities Value (QALYs) (SE) Source

MI (event - 11 days) 0.71 (0.08) (32)

Post-MI (chronic) 0.80 (0.20) (33)

Post-IS (asymptomatic/minor) 0.74 (0.25) (34)

Post-IS w/ Moderate Sequelae 0.65 (0.25) (34)

Post IS w/ Severe Sequelae 0.41 (0.38) (34)

Age-Specific Healthy Utilities

20-29 years 0.94 (0.01) (35)

30-39 years 0.94 (0.01) (35)

40-49 years 0.94 (0.01) (35)

50-59 years 0.93 (0.01) (35)

60-69 years 0.90 (0.01) (35)

70-79 years 0.86 (0.01) (35)

80+ years 0.70 (0.03) (35)

Table 2. QALY values for CVD events, post-CVD states, and age-specific QALYs for healthy individuals with no history of CVD.

2.13 Estimating Resource Use and Costs:

Estimating costs in model-based economic evaluation is a process of estimating resource-use for all relevant states, events, and treatments, and then assigning accurate unit costs to each resource. (36) Resource-use is estimated for CVD events and health outcomes (Table 3);

most estimations are made according to methods described in the NorCaD model. (22) NorCaD costs are well validated, and are frequently cited in Norwegian economic

evaluations and health technology assessments. (32,37–40) They are also often used by the Norwegian Medicines Agency in this context. (41)

Costs assigned to each individual resource or cost component are taken from publicly available information including: activity-based funding costs according to the Norwegian Directorate of Health (DRGs), the Normal Price Schedule for GPs and Emergency Care, and drug costs from the Norwegian Medicines Agency and Norwegian Prescription Database.

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Evolocumab and alirocumab are each taken as injections every two weeks. Evolocumab costs 16,200.70 NOK for six injections; the cost for one year—calculated as 52 weeks—is

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70,197.63 NOK. Alirocumab costs 15,933.20 NOK for six injections; one year costs 69,038.56 NOK. (41)

Table 3. Key Cost Parameters (all costs 2016 Norwegian Kroner)

Costs Value (NOK) Source

Cost of Stroke, first year (Event) 221,975 (22,32)

Post-IS 3,578 (22,32)

Moderate Sequelae 70,328 (22,32)

Severe Sequelae 1,006,484 ((22,32)

Cost of average MI 185,384 (22)

Reinfarction (same cycle) 35,782 (22)

Post-MI 3,578 (22)

CHD Death 59,315 (22)

One Unit of DRG 42,081 (42)

Evolocumab (1 year) 70,198 (41)

Alirocumab (1 year) 69,039 (41)

Ezetimibe (1 year) 4,759 (41)

Table 3. Total cost estimates for CVD events, post-CVD states, and treatments. All costs are total costs per year for states or events.

2.14 Choice of Model:

A state-transition Markov model was developed to model the incidence of atherosclerotic cardiovascular disease events—specifically myocardial infarction, death from coronary heart disease, and ischemic stroke. The model estimates incidence of ASCVD specifically within the Norwegian population, for both primary and secondary prevention settings. Individuals who are “well” are at risk for experiencing first-ever ASCVD events; those who survive these events transition into chronic post-CVD health states, where they remain at heightened risk for further events or death (Fig. 1). Note that “well” only indicates the absence of previous ASCVD events—these individuals may still be generally unhealthy and at a very high-risk for CVD due to various other baseline factors.

Each cycle length is one year and only one ASCVD event is possible per cycle—that is, a patient cannot suffer MI and IS in the same cycle. A cohort of patients can begin the model at any age from 30 years upward. The model runs up to age 100 or until everyone is dead. Men and women can be modeled in separate cohorts or combined. Baseline risk factors can be

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adjusted upwards to reflect heightened risk, while LDL-C reduction treatments reduce baseline risk according to the absolute measure of LDL-C reduction achieved. (6,7,22) A more detailed technical description of the model and its parameters is available in Appendix 1.

2.15 Model Assumptions

The model uses population-based incidence rates for CVD taken from Norwegian registry data and publications based on those registries; specifically, the Norwegian Cause of Death Registry and the Cardiovascular Disease in Norway registry were the two primary sources for baseline incidence rates. (2,45) The Swedish Stroke registry was used as well, in the absence of sufficient Norwegian-specific data; it was assumed that the Norwegian and Swedish populations are quite similar. (46)

Use of population-specific incidence rates, as opposed to more generalized risk equations, was chosen to reduce location bias and time bias; this decision is based on assumptions and arguments laid out in the NorCaD model and other analyses specific to cardiovascular disease

Figure 1. Simplified schematic of state transition Markov model. Rectangles represent health states while ovals represent events. Death is an absorbing state. Patients can move in the direction of any arrow. Looped arrows indicate a patient can remain in the health state for more than one continuous cycle. Patients can be in only one health state per cycle.

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in Norway. Risk equations, such as those employed in the US, UK, and Australia, are created for use in much broader populations and have not been validated for use in Norway. Some of these equations are based on older observational data and are known to overestimate risk in the Norwegian population as well. (22,47–50) Though it is specific to the Norwegian population, the NORRISK model was not used because it predicts only nonspecific cardiovascular death; it does not address nonfatal events or differentiate between types of CVD death, and as such would not have provided enough detail with which to model. (47) Therefore, the most recently available Norwegian data is considered to be the best reflection of the current incidence and risk of cardiovascular disease within the Norwegian population.

(22,47) Relative risks for those in post-CVD health states are adjusted upwards according to a wider range of sources; it is assumed that relative changes in risk as a result of manifest CVD are not population specific. (22)

2.16 Analytic Methods

Transition probabilities were derived from population-based incidence rates. Age- and sex- specific population incidence rates for first-ever MI, first-ever IS, CHD death, other CVD death, and all-cause mortality were taken from publicly available sources based on national registry data; the rates are therefore specific to and an accurate reflection of incidence rates for the entire population of Norway. (2,46,51) Age-bands used were as narrow as the available data allowed. Combination of sex-specific rates was performed according to the proportion of males vs. females within the population for each age-band. Rates were

converted to probabilities within the model according to methods given by Briggs et al. (20)

Baseline incidence rates are adjusted upwards and downwards to reflect varying levels of risk; relative risks are multiplied by incidence rates before conversion to transition

probabilities. (20) It was assumed that incidence rates based on national registry data would be a reflection of average baseline risk. Relative risk adjustments as a result of elevated baseline risk are based on the difference between the population average levels of risk and the heightened risk of the modeled cohort. Age- and sex-specific estimations of mean baseline risk factor levels for the population were taken from NorCaD. (22) Upward adjustments of relative risk for experiencing ASCVD were made according to the difference in baseline risk factors between the modeled cohort and the population mean. Baseline risk factors

considered are total cholesterol (TC), systolic blood pressure (BP), diabetes, and smoking.

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Relative risks for baseline factors are from NorCaD, which based its calculations on SCORE risk estimations. (1,22)

Absolute reductions in baseline LDL-C as a result of treatment were calculated according to difference-of-mean estimates taken from meta-analyses of PCSK9 inhibitor RCTs. (12–

14,19) Relative risk reductions as a result of treatment are modeled according to CTT analyses of the effect of LDL-C reductions on CVD risk. (6,7)

History of CVD results in elevated risk for further CVD events. (1,3) Individuals who

experience nonfatal CVD events and transition from the primary to the secondary component of the model are at increased risk for experiencing additional CVD events or death. Relative risks for those in chronic post-CVD states are taken from a variety of sources. (22,32,52–55)

Simpson’s 1/3 within cycle correction (WCC) was employed for all discounted and

undiscounted cumulative outcomes including life years, QALYs, costs, primary CVD events, and total CVD deaths. Within cycle corrections are recommended for state-transition models to adjust for the fact that discrete time steps are being used to model events that actually occur continuously. Simpson’s 1/3 WCC performs best relative to the gold standard and is recommended in this type of model. (56)

2.17 Sensitivity Analysis

One-way sensitivity analysis was performed through selectively adjusting certain parameters and then comparing the effects of this adjustment with the main deterministic results.

Specifically, this was performed on the cost per person of annual treatment with PCSK9 inhibitors, the effect of LDL-C reduction on the incidence of ischemic stroke, and the inclusion of production loss resulting from CVD death or disability in the costs.

Probabilistic sensitivity analysis (PSA) was undertaken according to methods laid out by Briggs et al. (20) This allows for the uncertainty around all parameters to be varied simultaneously in order to capture the overall uncertainty surrounding the output of the model. Beta distribution is used for rates, probabilities, and QALYs, and some resource-use measures; log-normal distribution is used for relative risks; and gamma distribution is used for costs. Monte Carlo simulation was performed in which the model was simulated 1000

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times using the random draws for each input parameter according to its respective

distribution; this provides the probabilistic output of the model and a clearer picture of the uncertainty surrounding point estimates and mean output. (20) Probabilistic output is recorded and analyzed within the net benefits framework, and presented as a Cost-

Effectiveness Acceptability Curve (CEAC). (20) Appendix 1 presents parameters and further details on PSA.

Expected Value of Perfect Information (EVPI) analysis was performed to estimate the value of reducing uncertainty through new research or more information; EVPI estimates are based on PSA output. Analysis of the Expected Value of Perfect Information for Parameters

(EVPPI) was also undertaken to determine for which specific parameters reduced uncertainty would yield the highest estimated value. (20)

2.18 Characteristics of Patient Groups and Heterogeneity

Four patient groups were used to represent potential high-risk individuals under consideration for initial use of PCSK9 inhibitors. These four groups correspond to those outlined in the previous section on ‘Patient Population and Subpopulations’ and baseline risk factors are a reflection of unmet clinical need (Table 4). Groups are listed in approximate descending order according to absolute CVD risk; groups 1 and 2 are at the highest absolute risk, while group 4 is at the lowest absolute risk—though lifetime CVD risk is very high for all patient groups. It is assumed that groups 1, 2, and 4 are taking the maximally tolerated intensity of statin regimen; group 3 is unable to tolerate any statins. General lifestyle advice is assumed given to all patients.

It is possible that real patients who correspond to these patient cohort groups have already initiated ezetimibe therapy, though only 6% of patients in Norway on lipid-modifying drugs take ezetimibe. (44) Each risk profile listed here is a reflection of total cholesterol and LDL- C levels in the absence of any non-statin lipid modifier. The difference between ezetimibe and PCSK9 inhibitors is then measured as the incremental cost and incremental effectiveness between the two, beginning at baseline levels that assume the absence of both.

Cost-effectiveness of PCSK9 inhibitors was tested across patient groups and ages for both primary and secondary prevention. Primary prevention is defined here as prevention for those

(32)

who have never suffered a previous myocardial infarction or ischemic stroke. Secondary prevention is defined as treatment and prevention for patients who have previously suffered myocardial infarctions—that is, all patients are post-MI.

Table 4. Patient Groups to be Analyzed and their Baseline Risk Factors No. Group Description Total Cholesterol

(mmol/L)

LDL-C (mmol/L)

Hypertension (SBP mm/Hg)

Diabetes Smoker

1 Diabetic 6.2 3.9 Y (145) Y N

2 HeFH 9.2 6.2 N N N

3 Statin Intolerant 7.3 4.9 N N N

4 Misc. High Risk 6.5 4.0 Y (145) N N

Table 4. Baseline characteristics and risk factors for patient groups analyzed in this report.

‘N’ denotes ‘No’ and ‘Y’ denotes ‘Yes’ to indicate respectively the absence or presence of a risk factor.

SBP denotes systolic blood pressure and is listed for hypertensive patients only.

HeFH indicates heterozygous familial hypercholesterolemia

(33)

3 Results

3.1 Cost of Treatment: PCSK9 Inhibitors vs. Ezetimibe

The cost of PCSK9 inhibitors over a lifetime is considerably higher than the lifetime cost of ezetimibe (Table 5 & Fig. 2). Total number of ezetimibe users has been steadily increasing from 2011 to 2015 (Fig. 3). It is possible that this trend will continue, as CVD and its risk factors are highly prevalent in Norway, especially as the population ages, pharmacological prevention for high-risk patients is continually stressed in clinical care, and recent studies have provided sound evidence for both effectiveness and cost-effectiveness of ezetimibe for preventing CVD. (1,3,18,47,57–60) Men account for slightly more use, probably because they are generally at higher risk, but the two sexes are quite similar. Most users are between the ages of 50 and 75, with the highest density of users between 65 and 69 years old (Fig. 4).

If all current ezetimibe users were to immediately switch to PCSK9 inhibitors, it would mean a 1,363% price increase overnight (Table 6). Assuming 30,000 ezetimibe users—if the

upward trend of users continues—the total cost in the first year would be over 2 billion NOK.

Table 5. Cost of PCSK9 Inhibitors and Ezetimibe per Person (NOK)

Years of Treatment: Cost of Ezetimibe: Cost of Alirocumab: Cost of Evolocumab:

1 year 4,759 69,039 70,198

5 years 23,794 345,193 350,988

10 years 47,589 690,386 701,976

25 years 118,972 1,725,964 1,754,941

50 years 237,944 3,451,928 3,509,882

Table 5. All costs undiscounted, in Norwegian Kroner (NOK), and current as of May 2016 Source: Norwegian Medicines Agency

(34)

Figure 2. Cost of Treatment with Ezetimibe vs. PCSK9 Inhibitors over Lifetime per Individual

All costs in Norwegian Kroner (NOK) and current as of May 2016; Source: Norwegian Medicines Agency

Figure 3. Total Ezetimibe Users in Norway by Year. Source: Norwegian Prescription Database 0

1 2 3

5 years 10 years 25 years 50 years

Cost per Individual (Millions of NOK)

Ezetimibe Alirocumab Evolocumab

0 5 000 10 000 15 000 20 000 25 000 30 000

2011 2012 2013 2014 2015

Number of Users

Total Men Women

(35)

Figure 4. Total Ezetimibe Users by Age in Norway as of 2015

Each listed age represents a five-year age band (e.g. 30 is 30-34), except 90 which is all users aged 90+

Source: Norwegian Prescription Database

Table 6. Total Cost for One Year of Treatment with PCSK9 Inhibitors vs. Ezetimibe (NOK) No. of

Users:

Cost of Ezetimibe:

(NOK)

Cost of PCSK9 Inhibitors:

(NOK)

Price Increase:

(NOK)

Price Increase:

(%) 25,880 123,159,556 1,801,716,282 + 1,678,556,726 + 1363%

30,000 142,766,100 2,088,542,831 + 1,945,776,731 + 1363%

Table 6. Total cost of each drug in Norwegian Kroner (NOK) based on number of ezetimibe users in Norway Sources: Norwegian Prescription Database and Norwegian Medicines Agency

3.2 Primary Prevention

Ezetimibe and PCSK9 each lead to QALY gains across all four patient groups (Table 7).

Given the highest density of ezetimibe users were found to be roughly 65 years of age, this age group will constitute the focus of the results. HeFH patients see the biggest gain in QALYs, from 8.74 with only statin therapy, to 9.50 when ezetimibe is added, and increasing again 10.34 with evolocumab. QALYs for diabetic patients are 7.31 with statins and increase to 7.83 with the addition of ezetimibe, and 8.57 with the addition of evolocumab.

Monotherapy for statin intolerant patients sees a slightly smaller gain, increasing from 9.90 with no treatment to 10.29 QALYs with ezetimibe, and then to 10.95 with evolocumab.

QALY gains for the miscellaneous high-risk group are slightly smaller. Alirocumab leads to similar but slightly smaller health gains than evolocumab for all patient groups.

0 1000 2000 3000 4000 5000 6000

30 35 40 45 50 55 60 65 70 75 80 85 90

Number of Users

Age

(36)

The cost per patient of treating manifest CVD decreases with each incremental treatment for all patient groups. Diabetic patients decrease from 419,000 NOK on standard treatment to 392,000 NOK with ezetimibe, and then drop again to 335,000 NOK with evolocumab.

Decreases in CVD costs for the other three patient costs are comparable, though the total CVD costs are highest for the diabetics. The biggest absolute cost reduction is observed in HeFH patients: 220,000 NOK with standard treatment drops to 182,000 NOK with the addition of ezetimibe and 122,000 with evolocumab. Alirocumab results in slightly less CVD costs saved than evolocumab for all patient groups.

Increases in lifetime drug costs per patient are quite substantial with both PCSK9 inhibitors.

The lowest absolute drug costs are for diabetic patients, who see an increase from 45,000 NOK with ezetimibe to over 700,000 NOK with either PCSK9 inhibitor. The highest drug costs are for the miscellaneous high-risk group, with an increase from 62,000 with ezetimibe to 927,000 and 949,000 NOK with alirocumab and evolocumab, respectively. Drug costs with PCSK9 inhibitors were over 1,000,000 NOK for every patient group amongst 50 year- olds (Table A2.1). Alirocumab is 20,000-25,000 NOK less expensive than evolocumab for all patient groups.

ICERs for the 65 year-olds were rarely below 1 million NOK/QALY. The most cost-effective patient groups tested amongst 65 year-olds were Diabetics and HeFH patients, with ICERs of 839,157 NOK/QALY and 905,378 NOK/QALY respectively. For 50 year-old patient groups, ICERs were well into the millions of NOK/QALY. The lowest ICERs were 1.3 million NOK/QALY and 1.345 million NOK/QALY for diabetics and HeFH patients respectively.

Alirocumab was extendedly dominated in every analysis. See Appendix 2 for more details (Table A2.2).

When analysis is stratified by gender, men have consistently lower ICERs than women (Table A2.3). For 50 year-old men, ICERs never drop below 1 million NOK/QALY; 65 year- old Diabetic and HeFH men see ICERs of 788,144 NOK/QALY and 739,341 NOK/QALY, respectively. 65 year-old Diabetic women are the only female group to see an ICER below 1 million, with 976,644 NOK/QALY.

(37)

Table 7. Primary Prevention for 65 year-olds (all numbers per person) Drug Cost

(NOK)

CVD Cost (NOK)

QALYs ICER

(∆NOK/∆QALY) Diabetics

Standard - 419,007 7.31 -

Ezetimibe 44,672 391,592 7.83 33,197

Alirocumab 700,148 345,793 8.45 Dominated

Evolocumab 722,505 334,973 8.57 839,157

HeFH

Standard - 219,602 8.74 -

Ezetimibe 54,425 181,194 9.50 21,163

Evolocumab 875,506 121,570 10.34 905,378

Statin Intolerant

Standard - 194,706 9.90 -

Ezetimibe 59,272 171,398 10.29 93,843

Evolocumab 932,233 120,554 10.95 1,241,197

Misc. High Risk

Standard - 186,674 10.33 -

Ezetimibe 61,657 162,921 10.67 108,718

Evolocumab 948,772 126,421 11.12 1,900,074

Table 7. Cost-Effectiveness results for primary prevention, across all patient groups at 65 years of age.

Primary prevention indicates that patients have no history of myocardial infarction or ischemic stroke.

Standard treatment reflects whatever statin regimen the patients were on prior to initiation of PCSK9 or ezetimibe therapy. No drug cost was used for standard treatment; it was assumed that statin regimens would not change according to treatment and would therefore have no bearing on an incremental comparison of costs.

3.3 Secondary Prevention

QALY gains in secondary prevention are quite similar to those observed in primary for all treatments, and in some cases slightly larger (Table 8). HeFH patients again have the biggest increase in QALYs, from 5.85 with only statins, to 6.85 with ezetimibe, and 8.01 with evolocumab. Statin intolerant increase from 7.07 QALYs with no treatment to 7.62 with ezetimibe and 8.56 with evolocumab. Alirocumab once again resulted in .10-.15 smaller QALY gains than evolocumab.

(38)

The cost of treating CVD for diabetics resulted in almost no decrease at all with ezetimibe;

with evolocumab it decreased from 579,000 NOK to 549,000 NOK. For each of the other three patient groups, cost of treating manifest CVD decreased by about 20,000 NOK with ezetimibe. CVD costs for HeFH patients decreased from 317,000 NOK to 256,000 NOK with evolocumab, and the statin intolerant and miscellaneous high-risk groups saw comparable though slightly smaller decreases. Evolocumab decreased costs by 7,000-10,000 NOK more than alirocumab in every patient group.

Lifetime drug treatment costs once again increase considerably from ezetimibe to PCSK9 inhibitors. Diabetic patients increase from 33,000 NOK to about 550,000 NOK, while miscellaneous high-risk patients increase from 50,000 NOK to over 800,000 NOK. The HeFH and statin intolerant groups see similar price increases. Evolocumab was 20,000- 30,000 NOK more expensive than alirocumab in every group for lifetime treatment.

Holding to a strict 600,000 NOK WTP threshold, PCSK9 inhibitors are only cost-effective for the HeFH patient group at 65 years of age with an ICER of 564,339 NOK/QALY. Use of PCSK9 inhibitors for 65 year-old diabetics is on the border of cost-effectiveness with an ICER of 610,896 NOK/QALY. Initiating PCSK9 therapy for those younger than 65 is not cost-effective in any patient group (Table 9). For 50 year-olds, ICERs are mostly over 1 million NOK/QALY; the most cost-effective group is HeFH patients with an ICER of 990,125 NOK/QALY (Table A2.4). The ICERs for those aged 70 and older are higher than ICERs for the 65 year-olds for all patient groups, but are still borderline cost-effective for the diabetic and HeFH patient groups.

When results are stratified by gender, ICERs for treating men are once again lower than those for treating women (Table 2A6). Treatment of 65 year-old HeFH men is unambiguously cost- effective with an ICER of 500,400 NOK/QALY. For 65 year-old HeFH women, the ICER is 699,378 NOK/QALY. 65 year-old diabetic women also move further away from the WTP threshold, but are still borderline cost-effective with an ICER of about 658,969 NOK/QALY;

the ICER for men in this group was essentially unchanged. Use of PCSK9 inhibitors for 65 year-old statin intolerant men is borderline cost-effective, with an ICER of 663,614

NOK/QALY, while women in this group are not cost-effective.

(39)

Table 8. Secondary Prevention for 65 year-olds (all numbers per person) Drug Cost

(NOK)

CVD Cost (NOK)

QALYs ICER

(∆NOK/∆QALY) Diabetics

Standard - 579,507 4.67 -

Ezetimibe 33,059 579,010 5.22 58,455

Evolocumab 566,966 549,343 6.05 610,896

HeFH

Standard - 336,587 5.85 -

Ezetimibe 43,442 317,028 6.85 23,707

Evolocumab 756,154 256,336 8.01 564,339

Statin Intolerant

Standard - 340,406 7.07 -

Ezetimibe 48,763 323,705 7.62 58,851

Evolocumab 814,484 267,308 8.56 754,206

Misc. High Risk

Standard - 344,768 7.55 -

Ezetimibe 51,800 324,366 8.05 62,255

Evolocumab 829,359 281,621 8.69 1,145,149

Table 8. Cost-Effectiveness results for secondary prevention across all patient groups at 65 years of age.

Secondary prevention indicates all patients have a history of myocardial infarction.

Standard treatment reflects whatever statin regimen the patients were on prior to initiation of PCSK9 or ezetimibe therapy. No drug cost was used for standard treatment; it was assumed that statin regimens would not change according to treatment and would therefore have no bearing on an incremental comparison of costs.

(40)

Table 9. Comparison of Cost-Effectiveness of PCSK9 Inhibitors vs. Ezetimibe across multiple Ages and One-Way Sensitivity Analysis of Price

Current Market Price (~70,000 NOK per person, per year):

Diabetic HeFH Statin Intolerant Misc. High Risk

Age: Primary: Secondary: Primary: Secondary: Primary: Secondary: Primary: Secondary:

50 Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe 55 Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe 60 Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe Ezetimibe 65 Ezetimibe Eze/PCSK9* Ezetimibe PCSK9 Ezetimibe Ezetimibe Ezetimibe Ezetimibe 70 Ezetimibe Eze/PCSK9* Ezetimibe Eze/PCSK9* Ezetimibe Ezetimibe Ezetimibe Ezetimibe 75 Ezetimibe Eze/PCSK9* Ezetimibe Eze/PCSK9* Ezetimibe Ezetimibe Ezetimibe Ezetimibe 50% of Price (~35,000 NOK per person, per year):

50 PCSK9 PCSK9 PCSK9 PCSK9 Ezetimibe Ezetimibe Ezetimibe Ezetimibe

55 PCSK9 PCSK9 PCSK9 PCSK9 Ezetimibe PCSK9 Ezetimibe Ezetimibe

60 PCSK9 PCSK9 PCSK9 PCSK9 Ezetimibe PCSK9 Ezetimibe Eze/PCSK9*

65 PCSK9 PCSK9 PCSK9 PCSK9 Eze/PCSK9* PCSK9 Ezetimibe PCSK9

70 PCSK9 PCSK9 PCSK9 PCSK9 Eze/PCSK9* PCSK9 Ezetimibe PCSK9

75 PCSK9 PCSK9 PCSK9 PCSK9 PCSK9 PCSK9 Ezetimibe PCSK9

25% of Price (~17,500 NOK per person, per year):

50 PCSK9 PCSK9 PCSK9 PCSK9 PCSK9 PCSK9 PCSK9 PCSK9

Table 9. Comparison of which treatment is the cost-effective alternative at different proportions of current price.

PCSK9 and Ezetimibe indicate which respective treatment is cost-effective; PCSK9 denotes evolocumab and/or alirocumab. Eze/PCSK9* indicates borderline cost-effectiveness, meaning 600,000 < ICER < 700,000 for one or both PCSK9 inhibitors. Secondary Prevention indicates patients have previously experienced myocardial infarction

(41)

3.4 One-Way Sensitivity Analysis of Price

One-way sensitivity analysis of price was performed to determine the most cost-effective strategy for each of the four patient groups across difference age levels, according to primary and secondary prevention, at several hypothetical price reductions.

As mentioned previously, ezetimibe was always cost-effective at a WTP of 600,000

NOK/QALY. At current market prices, ICERs for the PCSK9 inhibitors rarely came below 1 million NOK/QALY. Only secondary prevention in older patients with extremely high-risk indicated the possibility of cost-effectiveness (summarized in the upper-third section of Table 9).

With a 50% price reduction, PCSK9 inhibitors are cost-effectiveness for all diabetic and HeFH patients, and cost-effective or borderline for older patients and secondary patients in less severe risk groups (middle-third section of Table 9). At 25% of the current price, PCSK9 inhibitors are unambiguously cost-effective across all patient groups and ages tested (lower third of Table 9).

3.5 One-Way Sensitivity Analysis of the effect of Treatment on Stroke Incidence

This report makes several assumptions about the effects of LDL-C reduction on the relative risk of ischemic strokes. One-way sensitivity analysis was undertaken to determine the effect of these assumptions on the result in an effort to explore structural uncertainty.

3.5.1 First Assumption: Effect of LDL-C on Fatal Strokes

First assumption is that LDL-C reduction with any treatment does, in fact, reduce the relative risk of having a fatal ischemic stroke. According to CTT meta-analyses, the mean estimate for relative risk of fatal stroke per mmol/L of LDL-C reduced is 0.91 with a 95% confidence interval of 0.74 - 1.11. (6,7) The basic deterministic analysis of this report assumes that there is an effect on fatal IS and utilizes this 0.91 relative risk estimate, despite the fact that the results were considered non-significant at the 95% level. While the uncertainty around the mean estimate was explored as part of the overall probabilistic sensitivity analysis, one-way sensitivity analysis explores the effect of this assumption more directly.

(42)

Generally, the assumption has very little impact on any of the ICERs. The biggest potential impact for the decision problem is on 65 year-old diabetics in secondary prevention, who see a 40,000 NOK increase in an ICER that was already only borderline cost-effective (Table 10).

Alirocumab was extendedly dominated by evolocumab in every analysis and was therefore excluded from the results.

Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

Max Korman

Thesis submitted as a part of the Master of Philosophy Degree in Health Economics, Policy and Management

University of Oslo, The Faculty of Medicine,

Department of Health Management and Health Economics

Cost-Effectiveness Analysis of PCSK9 Inhibitors in Norway

Abstract

Acknowledgements

Table of Contents

1 Introduction and Background

2 Methods

2.1 The Decision Problem:

2.2 Hypothesis:

2.3 Patient Populations and Subpopulations:

2.4 Setting and Location:

2.5 Perspective:

2.6 Comparators:

2.7 Time Horizon:

2.8 Discount Rate:

2.9 Health Outcomes:

2.10 Cost-Effectiveness Outcomes:

2.11 Measurement of Effectiveness:

2.12 Measurement and Valuation of Preference-Based Outcomes:

2.13 Estimating Resource Use and Costs:

2.14 Choice of Model:

2.15 Model Assumptions

2.16 Analytic Methods

2.17 Sensitivity Analysis

2.18 Characteristics of Patient Groups and Heterogeneity

3 Results

3.1 Cost of Treatment: PCSK9 Inhibitors vs. Ezetimibe

3.2 Primary Prevention

3.3 Secondary Prevention

3.4 One-Way Sensitivity Analysis of Price

3.5 One-Way Sensitivity Analysis of the effect of Treatment on Stroke Incidence