Selection of first-line therapy in multiple sclerosis using risk-benefit

decision analysis

Table of Contents for Supplementary Material

appendix e-1. Disability state transition probabilities................................................................2

appendix e-2. Estimating EDSS-specific mortality rates...........................................................4

table e-1. Model inputs...............................................................................................................6

table e-2. British Columbia multiple sclerosis (BCMS) cohort characteristics..........................8

figure e-1. PML risk during natalizumab treatment...................................................................9

figure e-2. Sensitivity analyses varying MS-related mortality rates (k value)........................10

figure e-3. Sensitivity analysis varying natalizumab-associated PML risk..............................11

figure e-4. Sensitivity analyses varying the relative risk of disability worsening (RRworse).....12

e-References.............................................................................................................................13

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Appendix e-1. Disability state transition probabilities

Annual transition probabilities between disability states (EDSS 0 – 9) and the baseline EDSS state distribution were obtained from the UK Multiple Sclerosis Risk Sharing Scheme analyses.1,2 In these studies, values were derived from a subset (n=898) of untreated relapsing-remitting MS (RRMS) patients from the British Columbia MS (BCMS) database - a longitudinal natural history cohort. Data from the BCMS database were censored at the end of 1995, corresponding to the time when DMDs became widely available in British Columbia. Details of the selected subset can be found in Table e-1 and previous publications.1,2

To develop a working model, we rounded EDSS levels down to the nearest integer (e.g. EDSS levels 3 and 3.5 were grouped into a single state: EDSS 3).

Disability worsening was defined as ‘forward’ transitions to a higher EDSS during a yearly cycle. Treatment effects of disease-modifying drugs (DMDs) on disability worsening were accounted by adjusting untreated transition probabilities using the relative risk of disability worsening (RRworse) from the most recently published data from clinical trials with intention to treat. Trials were completed in 2005 and 2011. Relative risk was calculated by comparing the drug versus placebo in each case (see table below).

† Sustained disability worsening is defined as an increase of ≥1.0 point on the Expanded Disability Status Scale (EDSS) from a baseline score of ≥1.0 or an increase of ≥1.5 points from a baseline score of 0 for at least 3 months.

The annual relative risk of disability worsening (RRworse) was calculated using the proportion of patients with sustained disability worsening in 2 years in each drug or placebo control group. By assuming a constant rate of disability worsening, we first calculated the annual rate of disability worsening for each group (drug and placebo). We then used this rate to calculate the annual probability of disability worsening in each group. The relative risk compared the probability of disability worsening between the drug and its placebo control group.

Annual rate of disability worsening (r )=−ln (1−d) /t

Annual probability of disability worsening (Π)=1−e(−rt)

Annual relativerisk of disability worsening (RRworse)=Πdrug

Π placeboWhered is the proportion with sustained disability worsening in t yearsr is the annual rate of disability worsening Π is the annual probability of disability worsening

2

Drug Trial Trialcompletion

date

Proportion of patients with sustained disability

worsening in 2 years†

Annual relative risk of disability worsening(RRworse)Drug group

Placebo (control)

groupNatalizumab

(NTZ)Polman etal.2006/

AFFIRM 3 2005 0.17 0.29 0.565

Fingolimod (FGL)

Calabresi etal.2014/

FREEDOMS II 42011 0.15 0.19 0.780

Glatiramer acetate (GA)

Fox etal.2012/CONFIRM 5 2011 0.16 0.17 0.938

RRworse is the annual relative risk of disability worsening t is the time in years

The graphic below illustrates the adjustment of a ‘forward’ transition probability from initial state EDSSi to end state EDSS j, in a one-year period:

when i< j∧0≤ i<6 :

Pi , j (drug )=P i , j (untreated )∗RRworse (drug )

WherePi , j is the transition probability between an initial EDDS state i to end EDDS state j in a

one-year periodRRworseis the annual relative risk of disability worsening

For example, using the formula above and transition probability data in Table e-2, we can

calculate the probability of a patient receiving natalizumab (NTZ) treatment worsening from EDSS 4 to EDSS 5 in one year:

P4,5 (NTZ )=P4,5 (untreated )∗RRworse (NTZ )

P4,5 (NTZ )=0 .05518914∗0 .565

P4,5 (NTZ )=0 .0311818641=3 .1% per year

‘Backward’ transitions, representing disability improvement, were unchanged across all treatment groups. Patients who remained stable in the same EDSS state at the end of the year, neither improving nor worsening, made up the remaining probability value (i.e. Pstable=1−∑ P forward−∑ Pbackward),

In a given year, patients were assumed to be able to transition ± 0-3 EDSS states from their EDSS state in the preceding year.

Worsening in the model was defined as any ‘forward’ transition between EDSS state: i.e. any change from EDSS n at the start of the year to EDSS n+x (where x is an integer >0) at the end of the year. EDSS worsening outside of this range (>3 EDSS states in either direction) in a one-year period is rare as reflected by the transition probabilities (ranging from 2.0 x 10-8 to 0.036).

To reflect the changes in DMD efficacy at different phases of disease, each drug was assumed to have efficacy in delaying disease worsening when transitioning between states below EDSS 6 (corresponding to the relapsing-remitting phase of the MS disease) and to have no efficacy greater than placebo when transitioning between states above EDSS 6 (corresponding to the progressive phase of the disease). This is consistent with the available literature that none of the three drugs showed significant reduction in disability worsening in progressive MS.6-8

Pi , jEDSS jEDS S i

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Appendix e-2. Estimating EDSS-specific mortality rates

The mortality rate in MS patients is thought to be higher than the general population. Approximately half of this increased risk is attributable to MS directly, with the remainder due to a wide range of comorbidities (including infectious diseases, non-MS neurological diseases, cancer, respiratory and cardiovascular disease)9-14.

Previous MS decision analysis studies have modelled mortality rate using a constant value, independent of EDSS state and disease duration. These studies did not account for the higher MS-specific mortality due to longer disease courses and higher disability states (as measured by EDSS).

As we were unable to obtain EDSS-specific mortality rates from the literature, we derived the following estimation using available transition probabilities within the study cohort.

To estimate the transition from any EDSS state to death (EDSS 10) we assessed the probability distribution of all forward transition from an initial EDDS state, i, to an end EDSS state, j , in a one-year period (see Table e-2).

All forward transition probabilities from initial EDSS state i to end EDSS state j ¿¿ , i < j for forward transition, see blue-colored values in Table e-2) could be expressed using the exponential function:

Pi , j=ek (i− j)

WherePi , j is the transition probability between an initial EDDS state, i, to end EDDS state, j , in

a one-year periodk is a constant.

We then used a log-linear model (y¿ λ ekx ¿ to perform regression of all forward transition probabilities onto end EDSS states. By taking the natural logs of both sides (ln y¿kx+ ln λ¿ , we obtain a value of k = 1.2211 from the slope of the line (Pearson’s R2 = 0.88) (Figure e-2).

Using the exponential function above, we calculated transition probabilities from each EDSS state to EDSS 10. These values represent EDSS-specific annual mortality rate estimates:

Annual transition probabilities from each EDSS state to EDSS 10 (EDSS-specific mortality rate)

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Initial EDSS state Annual probability of transition to EDSS 10

0 e1 .2211(0−10)=¿0.000004981 e1 .2211(1−10)=¿0.000016872 e1 .2211(2−10)=¿0.000057213 e1 .2211(3−10)=¿0.000193994 e1 .2211(4−10 )=¿0.000657815 e1 .2211(5−10)=¿0.002230576 e1 .2211(6−10)=¿0.007563667 e1 .2211(7−10)=¿0.025647748 e1 .2211(8−10)=¿0.086969319 e1 .2211(9−10)=¿0.29490559

To address the uncertainty in our mortality rate estimates, we performed a one-way sensitivity analysis to assess the effect of a wide range of mortality rate values on our primary outcome. We varied the exponent k across a range of values (k = 0.1 – 10, corresponding to an annual mortality rate of ~90% – 0%) and showed that, across all values of k, the number of QALYs accrued over a 30-year period followed the same pattern: highest for NTZ (high PML risk) followed by FGL and then GA treatment (Figure e-2).

A 90% yearly mortality rate represents the upper bound of mortality estimate in this model and likely overestimates the actual (real life) risk. The k value could be further extended towards a value of 0 to achieve a mortality rate approaching 100%. However, the implication of 100% mortality (i.e., all patients at EDSS=9 at the start of the year are dead by the end of the year) would be unrealistic.

To ensure that all probabilities summed to 1 in each row of the matrix, probabilities in each row (EDSSi,19) were adjusted proportionally by the values in column EDSSi,10 (Table e-2, Transition Probabilities).

For end EDSS states other than 10, patients were assumed to be able to transition ± 0-3 EDSS states from their initial EDSS state each year.

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Table e-1. Model inputs

Model input Value Ref

Base case characteristics

Age at baseline (years)

Sex

Prior immunosuppression

JC virus antibody status

JC antibody titres

30

Female

No

Positive

1.5

Treatment Effect

Annualrelativeriskofdisabilityworsening†

Natalizumab

Fingolimod

Glatiramer acetate

0.565 (95% CI 0.394 to 0.804)

0.780 (95% CI 0.494 to 1.275)

0.938 (95% CI 0.581 – 1.520)

3,15

4

5

PML Risk

AnnualprobabilityofdevelopingPML

Natalizumab

Fingolimod

Glatiramer acetate

0.0085* (95% CI 0.0079 – 0.0089)

0.000018 (95% CI 0 – 0.0001)

0 (95% CI 0 - 0)

16

17,18

5

Utilities ††

EDSS 0

EDSS 1-1.5

EDSS 2-2.5

EDSS 3-3.5

EDSS 4-4.5

EDSS 5-5.5

EDSS 6-6.5

EDSS 7-7.5

EDSS 8-8.5

EDSS 9-9.5

EDSS 10 (Death)

0.9248

0.7614

0.6741

0.5643

0.5643

0.4906

0.4453

0.2686

0.0076

-0.2304

0

2

Mortality rate

Age-specific mortality rate

MS (EDSS –specific) mortality rate

United States Life table

appendix e-2

19

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* Highest reported PML risk in natalizumab-treated patients without prior immunosuppression.† Compared to no drug treatment (best-supportive care)†† Utilities are measured using Quality Adjusted Life Years (QALYs). A utility of 1 represents a year of perfect health whereas a utility of 0 would be equivalent to death. Negative values correspond to a state considered worse than death. Expanded disability status scale (EDSS) is an ordinal clinical disability rating scale ranging from 0 (normal neurological examination) to 10 (death due to MS) in half-point increments.

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Clinical characteristics

Female, n (% total)Age at onset, mean years (SD)EDSS at baseline, mean (SD)Time from symptom onset, mean years (SD)Number of confirmed relapses in last 2 years, mean (SD)

666 (74%)29.2 (8.7)2.44 (1.70)7.9 (6.9)2.9 (1.3)

Initial (baseline) EDSS state distribution

EDSS 0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10(MS death)

Patients (n=898) 80 159 130 146 65 88 60 38 14 24 8 64 0 0 0 0 0 0 0 0

Yearly transition probabilities (untreated patients)

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End

EDSS state, j

EDSS 0 EDSS 1 EDSS 2 EDSS 3 EDSS 4 EDSS 5 EDSS 6 EDSS 7 EDSS 8 EDSS 9

EDSS 0 0.69768390 0.18652290 0.07815141 0.02864687 0.00489648 0.00147162 0.00243396 0.00015797 0.00003294 0.00000196

EDSS 1 0.07181829 0.53066760 0.25587110 0.10234650 0.02153317 0.00620906 0.01061970 0.00076572 0.00015925 0.00000958

EDSS 2 0.02040541 0.18752470 0.42534500 0.24824340 0.05814612 0.02032107 0.03626017 0.00303947 0.00067384 0.00004080

EDSS 3 0.00736212 0.07259760 0.15222330 0.44650690 0.11964706 0.05695233 0.12710937 0.01412697 0.00325641 0.00021791

EDSS 4 0.00214630 0.02758406 0.06988985 0.14980750 0.49205308 0.05518914 0.17234562 0.02588382 0.00475601 0.00034459

EDSS 5 0.00069098 0.00881798 0.02731039 0.08495209 0.09314973 0.43361530 0.28812484 0.04020382 0.02232398 0.00081089

EDSS 6 0.00013753 0.00186172 0.00526399 0.02479217 0.03081845 0.04270324 0.71396304 0.14559780 0.03206125 0.00280079

EDSS 7 0.00000917 0.00014963 0.00046388 0.00208210 0.00586410 0.00307664 0.09118239 0.74991100 0.13667270 0.01058843

EDSS 8 0.00000073 0.00001188 0.00003805 0.00024304 0.00042883 0.00044195 0.01504979 0.05140540 0.90675250 0.02562780

EDSS 9 0.00000002 0.00000038 0.00000129 0.00001044 0.00001913 0.00001990 0.00095453 0.00343851 0.11758930 0.87796650

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Table e-2. British Columbia multiple sclerosis (BCMS) cohort characteristicsColored values (blue) represent ‘forward’ transition probabilities. These were adjusted according to the relative risk of disability worsening (RRworse) to inform drug-specific RRworse values. EDSS, Expanded Disability Status Scale.

Initial EDSS state, i

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Figure e-1. PML risk during natalizumab treatment

Progressive multifocal leukoencephalopathy (PML) risk during natalizumab treatment is dependent on JC virus antibody status and titre level, previous exposure to immunosuppressant therapy and duration of natalizumab treatment. Using calculated probability of PML from treatment surveillance data, we predefined three PML risk profiles: NTZ PML low risk (annual PML probability = 0.01%, corresponding to JCV negative status or JCV positive status with titres <1.5 and ≤2 years of NTZ treatment), NTZ PML medium risk (annual PML probability = 0.10%, corresponding to JCV positive status with titres >1.5 and ≤2 years of NTZ treatment or JCV positive status with titres ≤1.3 and 2-4 years of NTZ treatment) and NTZ PML high risk (annual PML probability = 0.85%, corresponding to JCV positive status with titres >1.5 and 4-6 years of NTZ treatment). Illustration adapted with permission from Barts MSers PML risk stratification (May 2014 release)20 using data taken from Plavina et al. 2014.16

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Figure e-2. Sensitivity analyses varying MS-related mortality rates (k value)

EDSS-specific mortality rates can be derived from available British Columbia Multiple Sclerosis cohort transition probabilities using an exponential function (see appendix e-2). The exponent value, k, is obtained from the best-fit line from linear regression of transition probabilities onto end EDSS states (A). We performed a one-way sensitivity analysis (B) varying the value of k across an entire feasible range (k = 0.1 – 10, for example, corresponding to an annual mortality rate from EDSS 9: ~90% – 0%). Varying the value of k does not alter the primary outcome: NTZ treatment confers the greatest QALYs after 30 years of treatment followed by FGL and then GA at all selected values (C).

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Figure e-3. Sensitivity analysis varying natalizumab-associated PML risk

This series of three-way sensitivity analyses assessed the effect on QALYs accrual over 30 years of varying the annual probability of natalizumab-associated PML (PPML) across three risk profiles (low PPML = 0.01%, med PPML = 0.1%, high PPML = 0.85%) while also varying the relative risk of worsening for natalizumab and fingolimod. Please also see figure e-1. The treatment conferring the maximal QALYs accrued was indicated by the color underlying the black spot (for the base case). JCV, John Cunningham virus; PML, progressive multifocal leukoencephalopathy.

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Figure e-4. Sensitivity analyses varying the relative risk of disability worsening (RRworse)

This series of three-way sensitivity analyses assessed the effect on QALYs accrual over 30 years varying the relative risk of disability worsening for natalizumab while also varying the relative risk of worsening for fingolimod (A-D) or glatiramer acetate (E-H). The treatment conferring the maximal number of QALYs is indicated by the color underlying the black spot (for the base case).

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