The Cost-Effectiveness of Treatment Options for Relapsing-Remitting and Primary Progressive Multiple Sclerosis: Modeling Analysis Plan October 7, 2016 Prepared for Institute for Clinical and Economic Review, 2016
Table of Contents 1. Approach... 2 2. Methods... 2 2.1 Overview and Model Structure... 2 2.2 Key Assumptions... 3 2.3 Population... 4 2.4 Treatments... 4 2.5 Input Parameters... 4 3. Tables... 8 4. Figures... 17 References... 18 Institute for Clinical and Economic Review, 2016 Page 1
1. Approach The primary aims of this analysis will be to estimate the cost-effectiveness of various treatments for (1) relapse-remitting and (2) primary-progressive multiple sclerosis patients (RRMS and PPMS, respectively). The model structure for this assessment is depicted in Section 4. The two models will be developed in Microsoft Excel. 2. Methods 2.1 Overview and Model Structure We will develope two Markov models for RRMS (Figure 1) and PPMS (Figure 2), with health states based on the Expanded Disability Status Scale (EDSS) 1, which has been widely used to describe MS progression in clinical trials 2. RRMS patients may progress to secondary progressive MS (SPMS) over their lifetime, therefore SPMS states will be included in the RRMS model. The models will be adapted from previously published work evaluating the cost-effectiveness of MS treatments 3-10. The RRMS model will consist of 20 health states: EDSS 0 9 for RRMS patients, EDSS 1 9 for SPMS patients, and death (Figure 1). At baseline, a cohort of patients will be distributed among the 10 RRMS health states according to the expected distribution of newly diagnosed MS patients. These patients will then transition between states during each one-year cycle over a lifetime time horizon, modeling patients from treatment initiation until death. The PPMS model will consist of 10 health states: EDSS 1-9 and death (Figure 2). Similarly, a cohort of patients will be distributed among the 10 PPMS health states, and will transition between states during each one-year cycle over a lifetime time horizon. For patients with RRMS, EDSS scores can increase, decrease or remain the same at each cycle. In SPMS and PPMS, EDSS scores can increase or remain the same, but will not be assumed to decrease. A patient can progress to death or have a relapse from any state. We will use a natural history transition matrix and apply a relative risk for each therapy to derive DMT specific transition probabilities between health states. A relative risk will also be applied to estimate EDSS and disease-modifying therapy (DMT) specific relapse rates. Utilities will be applied to each health state. Additionally, utility decrements will be applied for each relapse event, as well as for any adverse events (AEs). Outcomes and costs will be dependent on time spent in each health state, relapse events, AEs, and drug treatment. For each therapy, a total drug cost will be calculated including acquisition, Institute for Clinical and Economic Review, 2016 Page 2
administration, and monitoring costs. Each health state will have an additional associated cost, as will each relapse event. These direct costs will include inpatient care, ambulatory care, tests, prescription drugs other than DMTs, and investments in additional resources for care (e.g., a wheel chair and mobility services). Finally, a cost will be associated with the occurrence of adverse events. We will utilize a US health system perspective (i.e., focus on direct medical care costs only) with a 3% discount rate for costs and health outcomes. The model outcomes will be drug costs, adverse event costs, total costs, quality-adjusted life-years, life-years, and relapses. 2.2 Key Assumptions We will make several assumptions for this model Costs for each of the different EDSS-defined disease stages will be assumed to be the same across patients with RRMS, SPMS or PPMS. The discontinuation rate will be constant across all DMTs and EDSS levels. Patients will continue treatment after transitioning to SPMS states. Patients receiving DMT therapy will be assumed to stop treatment when their EDSS score reaches 7 or above. Patients who discontinue on initial treatment will be assumed to initiate second-line treatment. We will assume that second-line treatment is evenly distributed across natalizumab, fingolimod, and alemtuzumab. Patients who discontinue on second-line treatment will be assumed to follow the natural history progression of disease. No vial sharing will be assumed in the base case. Patients will have the same transition probabilities per health state regardless of the patient s disease history (Markov model assumption). Mortality multipliers by EDSS state will be the same for RRMS, SPMS, and PPMS. DMTs will have a direct effect on progression through EDSS states, conversion from RRMS to SPMS, and relapse rates. Treatments will have an indirect effect on mortality by delaying time to worse EDSS states, where risk of mortality is higher. Institute for Clinical and Economic Review, 2016 Page 3
2.3 Population The populations for these analyses will be adults ages 18 years and older in the United States with (1) RRMS [the model will include the potential to progress from RRMS to secondary-progressive MS (SPMS)], and (2) PPMS. Both populations will be previously naïve to DMTs. 2.4 Treatments The interventions for RRMS assessed in this model will be: ALE - Alemtuzumab (Lemtrada, Genzyme) DAC - Daclizumab (Zinbryta, Biogen and AbbVie Inc.) DMF - Dimethyl fumarate (Tecfidera, Biogen) FIN - Fingolimod (Gilenya, Novartis Pharmaceuticals Corp.) GA_C - Glatiramer acetate (Copaxone, Teva Neuroscience, Inc.) GA_G Glatiramer acetate (Glatopa, Sandoz, Inc.) IFNβ-1a_A - Interferon beta-1a (Avonex, Biogen) IFNβ-1a_R - Interferon beta-1a (Rebif, EMD Serono Inc.) IFNβ-1a_B - Interferon beta-1b (Betaseron, Bayer) IFNβ-1a_E - Interferon beta-1b (Extavia, Novartis Pharmaceuticals Corp.) NAT - Natalizumab (Tysabri, Biogen) OCR - Ocrelizumab (Ocrevus, Genentech Inc., Roche Group) PEG - Peginterferon beta-1a (Plegridy, Biogen) TER - Teriflunomide (Aubagio, Genzyme) The intervention for PPMS assessed in this model will be: OCR - Ocrelizumab (Ocrevus, Genentech Inc., Roche Group) 2.5 Input Parameters Population Demographics The modeled populations will have an assumed mean age at onset of disease of 29 years, mean weight of 79kg, and mean BSA of 1.92. 11 Institute for Clinical and Economic Review, 2016 Page 4
Natural History To evaluate progression of MS disease without a DMT, we will model the natural history of RRMS, SPMS, and PPMS. The initial distribution of patients with RRMS will be aggregated from several data sources to create a summary measure for implementation in the model (Table 1). 12-16 For the PPMS population the initial distribution of EDSS states from the ORATORIO 14 trial will be used (Table 2). The transitions between EDSS states in the absence of DMTs will be based on a previous study 8 that used data from the DEFINE and CONFIRM clinical trial supplementary data along with London- Ontario cohort data (Table 3). 12,13,17 The transition probabilities from RRMS to SPMS (Table 4), as well as within SMPS (Table 5), will be based on data from the London-Ontario cohort. 17,18 As there is not sufficient available data on PPMS transition probabilities, we will assume that PPMS transition probabilities are the same as SPMS transition probabilities (Table 6). Annualized relapse rates in the absence of DMTs will be based on an existing study that used data from the placebo group in the ADVANCE trial along with Patzold (1982) and Weinshenker (1989). 19-21 We will assume that PPMS patients do not experience relapses based on previous studies. Background mortality rates will be based on age-specific US life tables 22. These will be adjusted for MS-specific mortality using an EDSS-specific mortality multiplier calculated from Pokorski et al. 23 using the following equation: Multiplier=0.0219*EDSS3-0.1972*EDSS2+0.6069*EDSS+1 (Table 7). Treatment Efficacy Treatment efficacy with DMTs will be included in the model in two ways: 1) treatment effect on disability progression to higher EDSS states and progression from RRMS to SPMS, and 2) treatment effect on annual relapse rates. These relative risks will be acquired from a network meta-analysis (Table 8, methods presented separately). The treatment effect of OCR on disability progression to higher EDSS state was acquired from the ORATORIO presentation at the 31 st Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS). 24 We will assume that patients discontinue initial DMTs at a rate of 10% per year for the first 2 years of treatment, then at a rate of 3% per year until they reach EDSS 7, at which point all patients discontinue (Table 9). 10 After discontinuing, all patients will transition to second-line treatment. The efficacy of second-line treatment will be based on the average efficacy of NAT, FIN, and ALE commonly used drugs in this setting. 25 We will assume that patients discontinue second-line treatment at a rate of 10% per year until they reach EDSS 7, at which point all patients discontinue. Patients who discontinue second-line treatment will then follow the natural history progression. Institute for Clinical and Economic Review, 2016 Page 5
Costs An annual cost of care will be associated with each EDSS state. We will assume that these costs are the same for RRMS, SPMS, and PPMS. EDSS state costs will be calculated based on an interpolation of data from Kobelt et al. using the equation cost=4427.7*edss+27443 (Table 10). 26 Direct costs will include all direct medical costs except DMTs. A relapse will be associated with a direct cost of $2,339. 27 Indirect costs, which will be evaluated in a separate analysis, will include short-term absence, reduced working time, and early retirement. 26 Each DMT will be associated with an annual cost based on the wholesale acquisition cost (WAC), dosing, administration, and monitoring (Table 11 and 12). We will assume that annual costs for second-line therapy are an average of those for NAT, FIN, and ALE. 25 WAC for OCR was assumed to be the average of the three most recently approved DMTs (ALE and DMF) plus 10%. Infusion time was assumed to be one hour. All costs will be inflated to 2016 USD. Utilities Annual utility values per EDSS state will e based on previously published estimates that used data from the DEFINE and CONFIRM trials for RRMS and a UK survey from SPMS (Table 13). 8 Each relapse event will be associated with a disutility of 0.09. 27 We will assume that utility values for PPMS EDSS states are the same as SPMS. We will apply a disutility associated with AEs. We will either apply a per AE disutility and aggregate the total disutility by DMT or apply a single disutility for patients experiencing any AE and apply by DMT (Table 14). Adverse Events All DMTs will have associated AEs. We will include serious AEs that occurr in at least 1% of patients, as well as progressive multifocal leukoencephalopathy (PML). We will assume the same AE rates apply to both GA_G and GA_C as well as IFNβ-1b_B and IFNβ-1b_E. When AE rates from the lower dose are greater than AE rates for the higher dose, we will use AE rates from the lower dose. We will aggregate AE data per DMT into annual disutility and annual cost (Table 13). The cost of AEs will be based on the frequency of AEs by DMT multiplied by the cost per AE event.the cost per AE will be based on previously published data. 27 We will assume that AEs for second line therapy are an average of those for natalizumab, fingolimod, and alemtuzumab. 25 Institute for Clinical and Economic Review, 2016 Page 6
Model Outcomes The model will estimate the average amount of time patients spend in each health state. Unadjusted and utility-adjusted time spent in each health state will be summed to provide estimates of life expectancy and quality-adjusted life expectancy. Model outcomes of interest will include: By intervention: o Quality adjusted life expectancy (undiscounted and discounted) o Life expectancy (undiscounted and discounted) o Number of relapses Pairwise comparisons: o Incremental cost-effectiveness ratios for each intervention versus the standard no treatment o Incremental cost-effectiveness ratios for each intervention versus GA_G Sensitivity Analyses We will run one-way sensitivity analyses to identify the key drivers of model outcomes. Probabilistic sensitivity analysis will also be performed by jointly varying all model parameters over 5,000 simulations, then calculating 95% credible range estimates for each model outcome based on the results. We will also conduct a scenario analysis that included indirect costs. We will also perform threshold analyses comparing changes in drug prices across a range of incremental costeffectiveness thresholds. Institute for Clinical and Economic Review, 2016 Page 7
3. Tables Table 1. EDSS Distribution of Population of RRMS Patients Entering the Model EDSS State CONFIRM 12 DEFINE 13 OPERA I & II 14 TOWER & TEMSO 15 CARE II 16 TOTAL 0 13 15 15 18 21 29 24 51 5% 3% 280 4.4% 1 78 85 84 77 105 109 104 312 20% 21% 1385 21.8% 2 11 94 94 96 112 116 146 504 30% 28% 1805 28.4% 3 98 105 99 99 97 82 85 389 21% 25% 1540 24.3% 4 50 47 42 46 56 56 42 244 17% 16% 940 14.8% 5 13 12 11 14 16 16 14 145 7% 7% 396 6.2% 6 10 10 0.2% 7 0% 8 0% 9 0% Total 263 358 345 350 407 408 415 1655 1493 666 6345 100% Institute for Clinical and Economic Review, 2016 Page 8
Table 2. EDSS Distribution of Population of PPMS Patients Entering the Model EDSS State ORATORIO 14 1 0.1% 2 0.3% 3 26.5% 4 27.3% 5 15.7% 6 29.9% 7 0.1% 8 0.0% 9 0.0% Table 3. Annual Probability of Moving Between EDSS States for Patients with RRMS EDSS State at Start of Year EDSS State at End of Year 12,13,17,18 0 1 2 3 4 5 6 7 8 9 0 0.311 0.289 0.312 0.07 0.016 0.001 0 0 0 0 1 0.178 0.231 0.419 0.127 0.039 0.004 0.001 0 0 0 2 0.06 0.13 0.493 0.215 0.088 0.011 0.002 0 0 0 3 0.019 0.055 0.299 0.322 0.241 0.044 0.013 0.003 0.004 0 4 0.005 0.017 0.127 0.251 0.411 0.121 0.048 0.014 0.007 0 5 0.001 0.004 0.033 0.096 0.252 0.295 0.211 0.085 0.023 0 6 0 0.001 0.009 0.034 0.123 0.257 0.329 0.19 0.056 0.001 7 0 0 0.003 0.013 0.057 0.169 0.309 0.257 0.189 0.004 8 0 0 0 0 0 0 0 0 0.995 0.005 9 0 0 0 0 0 0 0 0 0 1 Institute for Clinical and Economic Review, 2016 Page 9
Table 4. Annual Probability of Conversion from RRMS to SPMS, by EDSS State Initial RRMS EDSS State Probability of transitioning to RRMS EDSS+1 in SPMS 8,17 1 0.003 2 0.032 3 0.117 4 0.21 5 0.299 6 0.237 7 0.254 8 0.153 9 1 Table 5. Annual Probability of Moving Between EDSS States for PPMS or SPMS EDSS State at End of Year 8,17 EDSS State at Start of Year 1 2 3 4 5 6 7 8 9 1 0.769 0.154 0.077 0 0 0 0 0 0 2 0 0.636 0.271 0.062 0.023 0.008 0 0 0 3 0 0 0.629 0.253 0.077 0.033 0.003 0.005 0 4 0 0 0 0.485 0.35 0.139 0.007 0.018 0 5 0 0 0 0 0.633 0.317 0.022 0.026 0.002 6 0 0 0 0 0 0.763 0.19 0.045 0.002 7 0 0 0 0 0 0 0.805 0.189 0.006 8 0 0 0 0 0 0 0 0.926 0.074 9 0 0 0 0 0 0 0 0 1 Institute for Clinical and Economic Review, 2016 Page 10
Table 6. Annual Relapse Rate, by EDSS States Initial EDSS State Relapse Rate, RRMS 19 Relapse Rate, SPMS 19 Relapse Rate, PPMS 0 0.26 0.00 0 1 0.24 0.00 0 2 0.46 0.32 0 3 0.50 0.60 0 4 0.67 0.52 0 5 0.18 0.16 0 6 0.15 0.14 0 7 0.16 0.10 0 8 0.16 0.10 0 9 0.16 0.10 0 Table 7. Calculated Mortality Multipliers of All-Cause General Population Mortality, by EDSS State, to be Applied to Age-specific Mortality Rates EDSS State Mortality Multiplier* 23 0 1.00 1 1.43 2 1.60 3 1.64 4 1.67 5 1.84 6 2.27 7 3.10 8 4.45 9 6.45 *Interpolated using the equation Multiplier=0.0219*EDSS3-0.1972*EDSS2+0.6069*EDSS+1 Institute for Clinical and Economic Review, 2016 Page 11
Table 8. Treatment Effect Parameters Treatment Relative Risk Disability Progression (Increasing EDSS and RRMS to SPMS)* Relative Risk for Relapse Rate (for RRMS/SPMS)* ALE 0.40 0.26 DAC 0.61 0.46 DMF 0.64 0.49 FIN 0.67 0.59 GA_C 20mg 0.70 0.59 GA_C 40mg 1.18 0.66 GA_G 0.70 0.59 IFNβ-1a_A 0.76 0.74 IFNβ-1a_R 22mcg 0.80 0.74 IFNβ-1a_R 44mcg 0.70 0.60 IFNβ-1b_B 0.63 0.60 IFNβ-1b_E 0.63 0.60 NAT 0.55 0.31 OCR (RRMS) 0.44 0.38 OCR (PPMS)** 0.75 N/A PEG 0.62 0.64 TER 7mg 0.85 0.79 TER 14mg 0.71 0.67 *From preliminary ICER network meta-analysis **Source: ORATORIO presentation at 2015 ECTRIMS congress. 24 Table 9. Treatment Discontinuation Rates During First-Line DMT Year of treatment EDSS State Annual probability of DMT discontinuation 10 1-2 0 6 10% 3+ 0-6 3% Any 7+ 100% Institute for Clinical and Economic Review, 2016 Page 12
Table 10. Annual costs per EDSS state. EDSS State Annual Direct Costs (2016 $)* 26 Annual Indirect Costs (2016 $)* 26 0 $8,146 $13,005 1 $9,460 $15,104 2 $10,774 $17,202 3 $12,089 $19,300 4 $13,403 $21,399 5 $14,717 $23,497 6 $16,032 $25,595 7 $17,346 $27,694 8 $18,660 $29,792 9 $19,974 $31,890 *Interpolated based on the equation cost=4427.7*edss+27443, separated into direct and indirect costs, and inflated to 2016 USD. Institute for Clinical and Economic Review, 2016 Page 13
Table 11. DMT Costs Product Name Active Ingredient Manufacturer/ Distributor Strength Package Size WAC Package Price* Dosing/Monitoring** Aubagio Teriflunomide Genzyme 14mg 28EA $5,706 14mg daily/ ALT monthly for 6 months, CBC test and LFT monthly for 6 months, then every 3 months Aubagio Teriflunomide Genzyme 7mg 28EA $5,706 7mg daily/ ALT monthly for 6 months, CBC test and LFT monthly for 6 months, then every 3 months Avonex Betaseron Copaxone Copaxone Extavia Interferon beta-1a Interferon beta-1b Glatiramer acetate Glatiramer acetate Interferon beta-1b Biogen 30mcg 0.5ml $5,821 30mcg weekly/ CBC and liver at 1,3,6 months, then periodic (2x/yr) Bayer 0.3mg 14EA $6,219 0.25mg every other day/ CBC and liver at 1,3,6 months, then periodic (2x/yr) Teva 20mg 30EA $6,593 20mg daily/ None Neuroscience, Inc. Teva 40mg 12EA $5,404 40mg 3x/week/ None Neuroscience, Inc. Novartis Pharmaceuticals Corp. Gilenya Fingolimod Novartis Pharmaceuticals Corp. Glatopa Glatiramer acetate Novartis Pharmaceuticals Corp. 0.3mg 15EA $5,558 0.25mg every other day/ CBC and liver at 1,3,6 months, then periodic (2x/yr) 0.5mg 30EA $6,743 0.5mg daily/ LFT every 6 months, CBC test every 2 months 20mg 30EA $5,194 20mg daily/ None Lemtrada Alemtuzumab Genzyme 10mg 1.2ml $20,244 12mg/day 3 days 12mg/day, 5 days, 1 year later / Monthly: CBC, serum creatinine, urinalysis, thyroid Ocrevus Ocrelizumab Genentech Inc., Roche Group 300mg N/A N/A 600mg every 3 months/ Monthly CBC Plegridy Rebif Rebif Tecfidera Peginterferon beta-1a Interferon beta-1a Interferon beta-1a Dimethyl fumarate Biogen 125mg 1ml $5,821 125mcg every 2 weeks/ CBC and liver function every 6 months EMD Serono Inc. 22mcg 0.5ml $6,284 22mcg 3x/week/ CBC and liver at 1,3,6 months, then periodic (2x/yr) EMD Serono Inc. 44mcg 0.5ml $6,284 44mcg 3x/week/ CBC and liver at 1,3,6 months, then periodic (2x/yr) Biogen 120mcg+ 240mcg, 240mcg 14EA+ 46EA, 60EA $6,315 120 mg twice a day for 7 days. After 7 days: 240 mg twice a day/ CBC every 6 months Tysabri Natalizumab Biogen 20mg 15ml $5,797 300mg every 4 weeks/ MRI every 6 months CBC+ LFT every month Zinbryta Daclizumab Biogen and AbbVie Inc. * Source: Redbook 2016, **Source: DMT package inserts 150mg 1ml $6,833 150mg injected monthly/ transaminase and bilirubin monthly Institute for Clinical and Economic Review, 2016 Page 14
Table 12. Estimation of DMT Costs Treatment Annual drug cost Annual administration cost Annual monitoring cost Annual drug cost Annual administration cost Annual monitoring cost Year 1 Subsequent Years ALE $101,219 $1,196 $2,432 $60,731 $718 $2,432 DAC $82,000 $2,329 $673 $82,000 $2,329 $673 DMF $76,833 - $70 $76,833 - $70 FIN $82,043 - $323 $82,043 - $323 GA_C 20mg $80,215 - $0 $80,215 - $0 GA_C 40mg $70,445 - $0 $70,445 - $0 GA_G $63,193 - $0 $63,193 - $0 IFNβ-1a_A $75,881 - $273 $75,881 - $182 IFNβ-1a_R 22mcg IFNβ-1a_R 44mcg $81,911 - $273 $81,911 - $182 $81,911 - $273 $81,911 - $182 IFNβ-1b_B $87,728 - $273 $81,065 - $182 IFNβ-1b_E $73,183 - $273 $67,625 - $182 NAT $75,568 $2,530 $5,277 $75,568 $2,530 $5,277 OCR $78,263 $776 $421 $78,263 $776 $421 PEG $75,881 - $182 $75,881 - $182 TER 7mg $74,380 - $1,106 $74,380 - $365 TER 14mg $74,380 - $1,106 $74,380 - $365 Table 13. Utility Scores by Health State EDSS State Annual Utility, RRMS 8 Annual Utility, SPMS/PPMS 0 0.8752 0.8660 1 0.8342 0.8250 2 0.7802 0.7710 3 0.6946 0.6855 4 0.6253 0.6161 5 0.5442 0.5350 6 0.4555 0.4463 7 0.3437 0.3346 8 0.0023-0.0068 9-0.1701-0.1793 Death 0 0 Institute for Clinical and Economic Review, 2016 Page 15
Table 14. Utilities and costs associated with adverse events per DMT. Rate Cost Utility Per Event Source Per Source Event IFNβ-1a_A IFNβ-1b_B GA_C 20mg GA_C 40mg IFNβ-1a_R 22mcg IFNβ-1a_R 44mcg PEG FIN TER 7mg TER 14mg DMF NAT ALE DAC OCR Lymphopenia* 0.01 $112 blood count + 1 specialist visit (assumed) ALT increased* 0.01 0.01 $224 2 specialist visits; 4 liver function tests (Mauskopf 2016) 0 Jakubowiak 2016 0 Mauskopf 2016 Cholelithiasis* 0.01 $4,477 hospitalization (assumed) 0.005 Cook 1994 Influenza* 0.01 $5,687 Hospitalization (Mauskopf 2016) 0.016 Mauskopf 2016 Serious infection* 0.01 $204 hospitalization (assumed) 0.005 Jakubowiak 2016 Trigeminal neuralgia* 0.01 $204 hospitalization (assumed) 0.44 Tölle 2006 Depression* 0.01 $4,108 2 specialist visits; hospital stay (Mauskopf 2016) 0.56 Mauskopf 2016 PML** 0.0003 $700 hospitalization + MRI (assumed) 0.4 Campbell 2013 Total Cost $0 $0 $0 $0 $154 $0 $0 $0 $3 $2 $3 $0 $0 $0 $0 Total Disutility 0 0 0 0 0.01075 0 0 0 0 0 0.00007 0.00012 0 0 0 *Rate source: trial aggregate >1% **Rate source: NAT package insert Institute for Clinical and Economic Review, 2016 Page 16
4. Figures Figure 1. Model structure for RRMS Figure 2. Model structure for PPMS Institute for Clinical and Economic Review, 2016 Page 17
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