FormalPara Key Points for Decision Makers

This cost-effectiveness analysis found that treating episodic (EM) and chronic migraine (CM) patients resulted in an incremental cost-effectiveness ratio (ICER) of ¥6,530,398. Results indicate that fremanezumab resulted in more QALY gain and a lower ICER for EM patients compared with CM.

Indirect costs for migraine patients such as productivity losses should be explored in cost-effectiveness analysis in Japan, as they are strongly related to the severity of the disease and have a substantial impact on the ICER.

The choice of distribution for modeling mean monthly migraine days can have a substantial impact on the ICER and should be carefully informed by statistical fit and experts’ opinions.

1 Introduction

Migraine is a neurological condition, characterized by a throbbing headache, nausea, vomiting, photophobia and phonophobia [1]. The disease disproportionately occurs more in women than men, affecting 6–8% of men compared with 15–18% of women each year [2,3,4]. Different from a headache, a migraine attack is associated with at least one of the symptoms mentioned above [1]. Furthermore, migraine can be classified into episodic migraine (EM; < 15 monthly headache days [MHDs] including ≥ 4 monthly migraine days [MMDs]) and chronic migraine (CM; ≥ 15 MHDs including ≥ 8 MMDs over the preceding 3 months) [1, 5].

In Japan, the United States (US), and Europe, nearly 75 million people have EM and CM [6]. In 2016, migraine was reported to be the fourth leading cause of years lived with disease in Japan [7]. Migraine is associated with personal pain, disability, a reduced health-related quality-of-life (HRQOL), and financial cost to society [8, 9]. The high economic burden of migraine is especially due to its costs associated with productivity loss [4, 8, 10,11,12], as migraine prevents people from going to work and is most common between the age of 25 and 55 years [10]. In Japan, an increasing amount of real-world data suggests that both EM and CM poses a notable disease burden and highlights high levels of unmet need with prescribed acute and preventative medication among people with migraine [13, 14].

Previously, migraine was treated with acute treatment such as painkillers (e.g., triptans and non-steroidal anti-inflammatory drugs) to relieve symptoms during a migraine attack and prophylactic treatment (e.g., β-blockers, anticonvulsant drugs, antidepressant drugs, selective serotonin and selective serotonin-norepinephrine reuptake inhibitors, calcium channel antagonists and neurotoxins) to reduce the severity, duration and frequency of attacks [15]. Until recently, preventive therapies have been repurposed from other indications, often with unknown working mechanisms, leading to a poor tolerability and lack of efficacy [16,17,18]. This in turn will lead to increasing discontinuation rates and dissatisfaction amongst patients [16,17,18].

Recently introduced monoclonal antibodies against calcitonin gene-related peptide (CGRP) and the CGRP receptor for preventive treatment of migraine have shown an improved tolerability, efficacy, safety and ease of use compared with conventional treatments [16, 17]. Fremanezumab is one of the available anti-CGRPs to treat migraine and has been proven effective in preventing EM and CM in two Japanese-Korean phase IIb/III clinical trials [19, 20]. Anti-CGRP monoclonal antibodies are more expensive compared with conventional treatment and should therefore only be prescribed if the improvement in effectiveness is high enough considering the costs [16, 17].

To our knowledge, only limited data exists on the cost effectiveness of fremanezumab compared with conventional treatment, consisting of acute treatment and prophylactics [21, 22]. For Japan, where conventional treatment primarily consists of acute treatment, no available evidence exists on the cost effectiveness of fremanezumab to inform treatment selection from a health economic perspective [23]. Also, there is no formal willingness-to-pay (WTP) threshold to determine whether a treatment is cost effective in Japan, and cost effectiveness is applied for price adjustment after reimbursement under current pricing rules. A range of 5–15 million Japanese Yen (¥) per quality-adjusted life-year (QALY) will be used for price adjustment [24].

The objective of the current research is to determine the cost effectiveness of fremanezumab compared with standard of care (SOC) in previously treated EM and CM patients from a Japanese healthcare perspective.

2 Methods

2.1 Overview

To estimate the effectiveness and cost effectiveness of fremanezumab in migraine patients, a decision analytic model is needed. The effectiveness of fremanezumab and placebo on the number of MMDs and HRQOL in EM and CM patients was captured in a previous study by Wang et al. using regression models [25]. For the current research, these regression models were implemented in a probabilistic Markov model that was developed in Microsoft Excel. This model was further populated with healthcare resource use (HCRU) and cost data and used to estimate the cost effectiveness of fremanezumab compared with SOC. An adjusted Japanese public healthcare perspective was adopted in which productivity losses were included, and the main model outcomes were life-years (LY), QALYs, costs in Japanese Yen (¥) inflated to 2022, and an incremental cost-effectiveness ratio (ICER). The current cost-effectiveness model (CEM) performed separate analyses for the EM and CM patients and merged the results for combined analyses.

2.2 Model Population

The model population was based on a cohort of Japanese EM (n = 357) and CM (n = 571) patients whose data was obtained from Japanese-Korean clinical trials [19, 20, 26]. The population consisted of patients who had a history of migraine for ≥ 12 months prior to trial admission. EM patients had 6–14 headache days with ≥ 4 migraine days (MDs), and CM patients ≥ 15 headache days with ≥ 8 MDs per 28 days. For the base-case analysis, only data from the Japanese patients was used. More details on the clinical trials are listed in the electronic supplementary material (ESM).

2.3 Treatments for Episodic and Chronic Migraine

In the clinical trials [19, 20, 26] that were used to estimate MMDs and HRQOL [25], patients were treated with fremanezumab 675 mg in the first month, followed by two doses of placebo in the second and third months. Alternatively, patients could also be treated with monthly administered fremanezumab 275 mg. Patients who were not treated with fremanezumab were treated with monthly administered placebo. Patients that were treated with both dosing schedules for fremanezumab were in the same intervention arm of the current CEM. The patients who received placebo were considered as the control arm (i.e., SOC) in the current CEM.

2.4 Model Structure

The disease course was simulated in the Markov model with three health states: alive (on treatment), alive (off treatment), and death (Fig. 1). Within each ‘alive’ health state, patients were partitioned over 29 MMDs per month (assuming 28-day months and 0 MMDs included) using the regression models from the previous modeling study [25]. Patients begin treatment, and after 3 months, either respond to and remain on treatment, or do not respond and discontinue (i.e. off treatment). ‘No response’ was defined as patients who did not achieve a 30% reduction in mean MMDs for CM and 50% for EM at 3 months. These definitions for no treatment response are considered clinically meaningful in the respective patient populations and were validated with Japanese clinical experts [27, 28]. The model population was simulated with 28-day cycles over a 25-years’ time horizon. Migraine is most common in younger and middle-aged people aged 25 years and older, and predominately effects women [2]. Since the course of the disease changes significantly after menopause, which on average occurs around the age of 50 years, the 25-year time horizon was deemed appropriate [29]. The Markov model was developed in Microsoft Excel.

Fig. 1
figure 1

Model structure. Patients could either be on treatment, off treatment or die. The arrow between on and off treatment represented the discontinuation rate and the arrows to the death state the general mortality rate. In each ‘alive’ health state the distribution of patients across based on mean MMDs was determined, which was different for patients that were treated with fremanezumab and SOC (i.e., off treatment). MMD monthly migraine day, SOC standard of care

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2.5 Model Input

2.5.1 Treatment Efficacy

Model input parameters are listed in Table 1. Results from the previous modeling study by Wang et al. [25] were used to inform treatment efficacy and utility data (for utilities see Sect. 2.5.3) in the current model. Parametric regression models were used to model mean MMD distributions for EM and CM patients treated with fremanezumab or SOC over a period of 3 months to inform health state transitions. For the base case, the zero-inflated beta binomial (ZIBB) distribution was used with only trial data from the Japanese participants [25]. This approach effectively modeled all 29 potential health states based on the number of MMDs during each model cycle. Due to a lack of long-term efficacy data, the model assumed that the treatment effect at 3 months was maintained as long as patients remained on treatment. This assumption was validated by clinical experts within the field of neurology (TT and FS). More details on estimating the MMDs can be found in the previous modeling study [25].

Table 1 Model input parameters
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Patients discontinued treatment when they did not adhere to the response criteria for fremanezumab in EM and CM and, therefore, a ‘negative’ stopping rule was applied. This concerned 50% of the EM and 25% of the CM patients that were treated with fremanezumab, which was based on clinical experts’ opinions (TT and FS). For each non-responder to fremanezumab, the treatment effect obtained by fremanezumab was replaced with the treatment effect for SOC. More details on treatment discontinuation can be found in the ESM.

Patients could also discontinue treatment for any reason [19, 20, 26]. An exponential parametric model was fitted to the observed data to estimate the discontinuation rate per model cycle (Table 1).

Migraine-related mortality was not modeled. However, given the 25-year time horizon, age-related mortality was included and modelled separately, using life tables from the Ministry of Health, Labor and Welfare in Japan [30].

2.5.2 Costs and Resource Use

Costs (2022 Japanese Yen [¥]), HCRU and discount rates were based on the Japanese C2H guidelines, clinical experts’ opinion and real-world patient survey data [31, 32]. Model costs and HCRU were chosen if they were expected to introduce any incremental differences between fremanezumab and SOC. The following cost categories were included in the model: treatment acquisition and administration costs, generalist visits, acute treatment costs, and productivity losses.

Treatment acquisition costs were applied to all patients that were on treatment with fremanezumab. The SOC drug acquisition costs were captured under resource use costs for patients in the fremanezumab arm that were off treatment and in the SOC arm. The SOC drug acquisition costs were calculated based on the JMDC database from Jan 2021 to Dec 2021 [33]. The ten most-sold medicines in the database were averaged (weighted by usage) and it was assumed that patients used these drugs once every migraine day in the model. An overview of these ten most-sold medicines is provided in the ESM. Treatment required a trained specialist to perform each administration. It was assumed that all patients had the injections administered in the base case. As such, all patients incurred an additional cost of ¥200 per administration [32]. Cost and HCRU for generalists visits were obtained from the literature [32]. The HCRU was obtained for 1 MMD and extrapolated linearly as the number of MMDs increased. Acute treatment costs were equal to SOC acquisition costs and applied to the fremanezumab arm. The HCRU of acute treatment costs was assumed to be 1 per MMD and also extrapolated linearly per MMD. Finally, productivity losses were included in the base-case analysis of the model because of the strong relationship between migraine and productivity [8]. The productivity losses were calculated using the annual salary for men and women in Japan multiplied with the percentage of work time missed (absenteeism) and impairment while working (presenteeism). The absenteeism and presenteeism differed between the number of MMDs based on the literature [34]. This calculation resulted in the annual productivity loss which was recalculated to monthly costs and applied to each model cycle. Costs and effects were discounted with an annual discount rate of 2% [31]. More details on productivity losses are listed in the ESM.

2.5.3 Utilities

MMD-specific utility values were used that were also estimated in the previous modeling study [25]. In that study, utility values were obtained from the Japanese-Korean clinical trials [19, 20, 26], with the migraine-specific quality-of-life (MSQ) questionnaire and mapped to EuroQol–five dimension–three level (EQ-5D-3L) values [25]. The MSQ-derived EQ-5D-3L values were modelled as a function of MMDs using a linear regression model, resulting in specific values for each health state in the current Markov model [25]. Different from the previous modeling study, treatment-specific utilities were assumed in the base case by incorporating treatment as a covariate in the regression model. This was advised by clinical experts within the field of neurology (TT and FS) and health technology assessment (AI) as they believed that the number of MMDs should not be the only factor that impacts HRQOL, but also factors such as the length and severity of migraine. More details on the utilities can be found in the ESM.

2.6 Analyses

2.6.1 Base-Case Analysis

A deterministic base-case analysis was performed using the point estimates of each model parameter. Incremental LY, QALY and costs were calculated between fremanezumab and SOC. The ICER was calculated using the incremental QALY and costs but there was no specific WTP threshold in Japan as a reference.

2.6.2 Sensitivity Analyses

Several sensitivity analyses were performed to test the robustness and uncertainty of the model. Deterministic one-way sensitivity analyses (OWSA) were performed to test the influence of each individual parameter on the model outcomes. Parameter values were varied on their standard error, which was estimated based on the input’s source, or set to 20% of the mean if not available.

Probabilistic sensitivity analyses were performed in Microsoft Excel Visual Basic for Applications (VBA) to evaluate uncertainty of all model parameters simultaneously. This was done by drawing random values for each model parameter from prespecified distributions using Monte Carlo simulations with 2000 iterations. Model convergence was tested to determine if this was enough iterations for the PSA to produce robust outcomes. More details on model convergence can be found in the ESM.

Scenario analyses were performed to explore the impact of certain model settings and assumptions on the results. Scenario 1 included both the Japanese and Korean clinical trial data to model the effectiveness of fremanezumab and SOC (both on mean MMDs and utilities) and explore the difference compared with using Japanese-specific data only. Scenario 2 removed productivity losses from the analysis, since the Japanese healthcare perspective does not typically include indirect costs. In Scenario 3 and Scenario 4, the zero-inflated negative-binomial (ZINBI) and zero-inflated gamma (ZAGA) models were used to model the mean MMD distributions over time, respectively, and explore the impact of the selected parametric model distribution. Scenario 5 and Scenario 6 were a combination of Scenario 2 and Scenario 3 and Scenario 2 and Scenario 4, respectively. In Scenario 7, a time horizon of 5 years was adopted to estimate the short-term impact of fremanezumab on costs and effects. Scenario 8 included a positive stopping rule for patients to discontinue treatment after 1 year if they have not experienced loss of response. It was assumed that this concerned 10% of the EM and CM patients and that the treatment effect linearly waned back to baseline (i.e., treatment effect at month 0) over a period of 1 year.

2.7 Model Validation

The model was checked for face validity and assumptions were validated by experts within the field of neurology (KY, FS) and health economic outcomes research (AI). Model input parameters, outcomes and scenarios analyses were also validated by the clinical physicians (KY, FS, AI). The model was also checked for validity by one of the authors (MS) using the Assessment of the Validation Status of Health Economic decision models (AdViSHE) checklist [37]. The completed checklist can be found in the ESM.

3 Results

3.1 Base Case

The model results showed that in the EM population, patients had 2624 MMDs in total while treated with fremanezumab compared with 2789 MMDs with SOC (increment of 83.3). For the CM population, the total MMDs amounted to 4882 for fremanezumab and 4965 for SOC (increment of 164.9). The total population (consisting of 70% EM and 30% CM patients) had in total 3301 MMDs while treated with fremanezumab and 3442 with SOC (increment of 140.4).

The main deterministic base-case results are listed in Table 2. The mean QALYs over the entire time horizon for the total population (consisting of 70% EM and 30% CM patients) were 13.03 for SOC and 13.15 for fremanezumab. The mean costs over the entire time horizon for the total population were ¥27,550,292 for SOC and ¥28,371,048 for fremanezumab. The total QALYs for EM patients were higher and the total costs lower compared with the CM patients for both fremanezumab and SOC (Table 2). The ICER was ¥6,334,861 for the EM and ¥7,393,824 for CM patients, while for the total population it amounted to ¥6,530,398. Additional intermediate base-case results are listed in the ESM.

Table 2 Total mean outcomes per patients of the deterministic base-case analysis
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3.2 One-Way Sensitivity Analysis

In general, results of the OWSA showed that the model results were robust (Fig. 2). However, the percentage of work time missed for several different numbers of MMDs and for both on and off treatment had quite some impact on the ICER, especially in EM patients. Varying the percentage of work time missed for EM patients with 4–7 MMDs who were off treatment based on the SE, resulted in an ICER range of −¥1,482,707 to ¥14,010,238. Furthermore, varying the treatment acquisition costs of fremanezumab resulted in an ICER range of ¥2,934,538 to ¥10,896,186. Additional results on the OWSA can be found in the ESM.

Fig. 2
figure 2

Tornado plot of the one-way sensitivity analysis results of fremanezumab versus standard of care on the incremental cost-effectiveness ratio. ICER incremental cost-effectiveness ratio, MMDs monthly migraine days

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3.3 Probabilistic Sensitivity Analysis

Results of the PSA are shown in Table 3, Fig. 3, and Fig. 4. The incremental outcomes showed that fremanezumab always resulted in more QALYs (0.12 [credible interval: 0.07–0.19]), however, in a few cases in less costs (¥961,080 [−¥301,302 to ¥2,210,494]) compared with SOC. The probabilistic ICER of fremanezumab versus SOC was ¥7,958,648. Based on the PSA results, fremanezumab had a 51.0% and 93.2% chance of being cost effective compared with SOC, if the WTP per QALY were ¥8,320,000 and ¥15,040,000, respectively (Fig. 4). Fremanezumab had about a 26.0% chance of being cost effective compared with SOC, if the WTP was ¥5,000,000.

Table 3 Results of the probabilistic base-case analysis
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Fig. 3
figure 3

Incremental cost-effectiveness plane of fremanezumab versus standard of care. QALY quality-adjusted life-year, SOC standard of care

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Fig. 4
figure 4

Incremental cost-effectiveness ratio of fremanezumab versus standard of care for an increasing willingness-to-pay threshold. QALY quality-adjusted life-year, SOC standard of care

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3.4 Scenario Analysis

Results of the scenario analysis are listed in Table 4. Using both Japanese and Korean clinical trial data (Scenario 1) resulted in slightly more incremental costs and a comparable ICER compared with the base case, indicating that the data of both nationalities were comparable. Excluding the productivity losses (Scenario 2) resulted in an increase in incremental costs and a higher ICER, as more indirect costs were accrued by patients treated with SOC compared with fremanezumab. Using the ZINBI distribution to model the mean MMDs (Scenario 3) resulted in slightly lower incremental costs and a comparable ICER compared with the base case, as this model resulted in slightly more patients having fewer MMDs compared with the ZIBB model. Using the ZAGA distribution to model the mean MMDs (Scenario 4) resulted in less incremental costs and a lower ICER compared with the base case, as this model resulted in more patients having fewer MMDs compared with the ZIBB model. Combining Scenario 2 with 3 (Scenario 5) and Scenario 2 with 4 (Scenario 6) both resulted in incremental costs and ICERs that were higher than the base case and comparable with Scenario 2, indicating that the exclusion of productivity losses had the most influence on the outcomes. Shortening the time horizon to 5 years (Scenario 7) resulted in less incremental QALYs and costs and a comparable ICER compared with the base case. This indicates that the most gain in QALYs and costs of fremanezumab compared with SOC takes place in the first few years and differences between the two treatment arms become less as we move further in time. Applying a positive stopping rule to patients that still respond to treatment after a year (Scenario 8) resulted in less incremental costs and a lower ICER compared with the base case. This is because patients that discontinue fremanezumab because of this positive stopping rule slowly wane back to baseline in terms of treatment effect, while they immediately stop accruing treatment costs.

Table 4 Deterministic results of the scenario analysis of the pairwise comparison of fremanezumab versus placebo
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4 Discussion

The current study determined the cost effectiveness of fremanezumab compared with SOC in Japanese EM and CM patients. Regression models were used to model the treatment effect of fremanezumab on MMDs and HRQOL [25]. The model used a Japanese public healthcare perspective but also included productivity losses in its base-case analysis. The deterministic ICER of fremanezumab compared with SOC was ¥6,334,861 for EM, ¥7,393,824 for CM and ¥6,530,398 for the total population. Our findings showed that fremanezumab compared with SOC always resulted in more QALYs but in a few cases in less costs with a probabilistic ICER of ¥7,360,884 for EM, ¥10,095,698 for CM and ¥7,958,648 for the total population. Using different parametric distributions to model the mean MMDs resulted in better ICERs compared with the base case (Scenario 3: ¥6,110,871 and Scenario 4: ¥5,702,725), as well as including a positive stopping rule (Scenario 8: ¥5,660,814). The highest ICERs were found in the scenarios in which productivity losses were excluded from the analysis (Scenario 2: ¥9,952,007, Scenario 5: ¥9,525,130, and Scenario 6: ¥9,442,917). Given the WTP threshold range that is used for price adjustment, all ICERs of the base-case analysis and scenario analyses were within the range of ¥5.0 to ¥15.0 million. Of course, depending on what threshold is used, these results could suggest that fremanezumab is cost effective compared with SOC. However, if a WTP threshold of ¥5.0 million were adopted, fremanezumab would only have about a 26% chance of being cost effective compared with SOC.

To our knowledge, fremanezumab has been evaluated and proven to have met the WTP threshold in numerous countries [21, 22]. However, it is hard to compare CE studies due to different assumptions, parameter inputs and comparators.

These results came with some assumptions regarding extrapolating the treatment effect on MMDs and utilities beyond the observed effectiveness data. We explored the impact of choosing different parametric distributions on the main outcomes in scenario analyses. The ZINBI distribution produced a slightly better ICER compared with the base case, while the ZAGA produced a substantially better ICER. This was because the ZINBI and ZAGA models estimated more patients with less MMDs compared with the ZIBB. The study by Wang et al. (2023) found that the ZINBI distribution had a comparable statistical fit to the ZIBB model for EM patients based on the Akaike information criterion (AIC) [25]. This was not the case for CM patients, nor for the ZAGA model for both EM and CM patients. Ultimately, the ZIBB was chosen as the best fitting model according to expert validation and therefore was selected for the base-case analysis of the current research [25]. However, due to convergence problems with our data in a few PSA iterations and the assumed uncertainty around the indirect costs (main driver of the ICER), the ZIBB model estimated a mean probabilistic ICER that was higher than the deterministic one.

According to a clinical expert within the field of neurology (FS), the effects of anti-CGRP are groundbreaking, as many patients experience a significant reduction in their disability in clinical practice. The rate of improvement in the degree of disability is comparable to reports from Europe and the US [21, 22, 38]. Many patients realized that they had a high level of disability only after the disability was improved.

A limitation of the current study was that we were not able to model HCRU as a function of MMDs as was done for HRQOL in the previous study by Wang et al. [25]. Evidence suggests that model parameters like medication use and productivity are non-linearly related to the number of MMDs [4, 8]. Capturing the relationship between these parameters and MMDs would require clinical trial data that includes data on effectiveness, HCRU and productivity. Also, there was no data available on long-term effectiveness of fremanezumab in EM and CM patients. Therefore, we assumed that the treatment effect at 3 months remained the same as long as patients stayed on treatment. This assumption was validated by clinical experts within the field of neurology but has to be confirmed when long-term data becomes available.

A strength of the current research is that, to our knowledge, we were the first to estimate the cost effectiveness of fremanezumab compared with SOC in EM and CM patients in Japan. Also, we were able to model the MMDs as a continuous variable using regression models. This made it easy to link model parameters to each number of MMDs and add granularity without having a computationally burdensome or unwieldy model with many health states and transition probabilities. This was proven by the fact that the sensitivity analysis only took a couple of minutes to run despite the fact that we were able to model a high level of detail regarding MMDs.

Our probabilistic results suggest that fremanezumab is cost effective compared with SOC in EM and CM patients if there is at least a WTP of ¥7,958,648 per QALY gained. Our deterministic results also showed that fremanezumab resulted in more QALYs in the EM patient population compared with CM and cost effectiveness is reached earlier in EM patients given the lower ICER. Also, productivity losses should not be negated in EM and CM patients as this is strongly connected to the disease [4, 8, 10,11,12]. Excluding it from the analyses has a substantial impact on the ICER, which makes it likely that the model results would be more removed from the reality whereby patients are impaired from work by migraine. Furthermore, our results suggest that modeling the effectiveness of fremanezumab on MMDs using different regression models enables the desired level of detail to be modeled while maintaining ease of use. While using different parametric distributions can have substantial impact on the ICER, it shows the importance of carefully informing the selection by statistical fit and expert opinions.

Future research should be focused on long-term effectiveness of fremanezumab in EM and CM patients in Japan to better inform CEMs. It would also be interesting to estimate model parameters such as HCRU or productivity losses as a function of MMDs to capture the relationship of these parameters.

5 Conclusions

Using fremanezumab in a heterogeneous mixture of Japanese EM and CM patients resulted in a reduction of MMDs and thus more QALYs compared with SOC. Additionally, use of fremanezumab was associated with reductions in migraine-related HCRU and associated costs, and an improved work productivity. The cost-effectiveness ratio of fremanezumab versus SOC in EM and CM patients was ¥6,530,398, from an adjusted Japanese public healthcare perspective. Indirect costs are strongly related to diseases like migraine and should be explored in cost-effectiveness analysis in Japan.