BMJ Open...existing published studies to draw meaningful comparisons between the efficacy of DMTs in...

For peer review only

A systematic literature review and network meta-analysis in

highly active relapsing remitting multiple sclerosis and

rapidly evolving severe multiple sclerosis in the UK

Journal: BMJ Open

Manuscript ID bmjopen-2016-013430

Article Type: Research

Date Submitted by the Author: 11-Jul-2016

Complete List of Authors: Huisman, Eline; Mapi Group Papadimitropoulou, Katerina; Mapi Group Jarrett, James; Mapi Group

Bending, Matthew; Mapi Group Firth, Zoe; Mapi Group Allen, Felicity; Novartis Pharmaceuticals UK Ltd Adlard, Nick; Novartis Pharmaceuticals UK Ltd

<b>Primary Subject Heading</b>:

Pharmacology and therapeutics

Secondary Subject Heading: Qualitative research, Neurology, Health economics

Keywords: Fingolimod, Natalizumab, Dimethyl fumarate, Network meta-analysis, RRMS

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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A systematic literature review and network meta-analysis in highly active

relapsing remitting multiple sclerosis and rapidly evolving severe multiple

sclerosis in the UK.

Eline Huisman1, Katerina Papadimitropoulou1, James Jarrett2, Matthew Bending2, Zoe

Firth2, Felicity Allen3, Nick Adlard3

1Mapi Group, De Molen 84, 3995 AX Houten, Netherlands

2Mapi Group, 73 Collier Street, London N1 9EB, UK

3Novartis Pharmaceuticals, Frimley Business Park, Frimley, Camberley, Surrey GU16

7SR, UK

Word count: 3808

Corresponding author contact details:

Nick Adlard

Novartis Pharmaceuticals UK Limited

200 Frimley Business Park

Frimley, Camberley, Surrey GU16 7SR

UNITED KINGDOM

Phone: +44 7741 292 178 [email protected]

Keywords: Fingolimod, natalizumab, dimethyl fumarate, network meta-analysis, RRMS

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ABSTRACT

Objective: Multiple sclerosis (MS) is a chronic, neurodegenerative autoimmune disorder

affecting the central nervous system. Relapsing Remitting MS (RRMS) is the most

common clinical form of MS and affects approximately 85% of cases at onset. Highly

active (HA) and rapidly evolving severe (RES) RRMS are two forms of RRMS amenable to

disease modifying therapies (DMT). This study explored the efficacy of fingolimod

relative to other DMTs for the treatment of HA and RES RRMS.

Methods: A systematic literature review (SLR) was conducted to identify published

randomised controlled trials (RCTs) in HA and RES RRMS. Identified evidence was

vetted, and a Bayesian network meta-analysis (NMA) was performed to evaluate the

relative efficacy of fingolimod versus dimethyl fumarate (DMF) in HA RRMS and versus

natalizumab in RES RRMS.

Results: For HA RRMS, the SLR identified two studies with relevant patient subgroup

data: one comparing fingolimod with placebo and the other comparing DMF with

placebo. Three studies were found for RES RRMS: one comparing fingolimod with

placebo and two studies comparing natalizumab with placebo. NMA results in the HA

population showed a favourable numerical trend of fingolimod vs. DMF assessed for ARR

and three-month confirmed disability progression. For the RES population, the results

identified an increase of ARR and 3-month confirmed disability progression for fingolimod

vs. natalizumab (not statistically significant). Sparse study data and the consequently

high uncertainty around the estimates restricted our ability to demonstration statistical

significance in the studied subgroups.

Conclusion: Data limitations are apparent when conducting an informative indirect

comparison for the HA and RES RRMS subgroups as the subgroups analyses were

retrospective analyses of studies powered to indicate differences across entire study

populations. Comparisons across treatments in HA or RES RRMS will be associated with

high levels of uncertainty until new data is collected for these subgroups.

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STRENGTHS AND LIMITATIONS OF THIS STUDY

• Comprehensive and robust search strategy developed to identify all relevant

interventions for the treatment of RRMS.

• Potential bias in the analyses since the baseline characteristics of the HA and RES

subgroups could not be adequately evaluated in some studies

• Accordingly, studies were only synthesised if the patient populations used the

same definitions for HA and RES RRMS, which limited the impact of potential

imbalances on NMA results.

• The limited evidence base prohibited adjustments for potential treatment effect

modifiers.

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INTRODUCTION

Multiple sclerosis (MS) is a disease of the central nervous system where myelin within

the brain or spinal cord becomes inflamed and is then destroyed by the immune

system.[1] It can be classified into three subtypes: relapsing and remitting MS (RRMS),

secondary progressive MS and primary progressive MS. RRMS is the most common

clinical form of MS and accounts for approximately 85% of cases at onset.[2] In RRMS,

people have distinct attacks of symptoms which then fade away either partially or

completely. Symptoms may not all be experienced at the same time but can include

visual disturbance, lack of balance and dizziness, chronic fatigue, bladder problems,

pain, muscle weakness or spasticity and cognitive impairment.[3]

Although there is still no cure for MS, research has shown major improvements in MS

treatment over the last 20 years and multiple disease modifying treatments (DMT) have

become available since then, including interferon beta, glatiramer acetate, teriflunomide,

dimethyl fumarate, natalizumab, fingolimod, and alemtuzumab.[1,4] Many of these

treatments focus on early phases of the disease, fewer treatment options are available

for patients with highly active (HA) or rapidly evolving severe (RES) RRMS. Data on

populations of patients with HA and RES RRMS are the subject of the analysis. At the

time of this study, HA RRMS was defined in the fingolimod label as an unchanged or

increased relapse rate or on-going severe relapses compared with the previous year

despite treatment with at least one DMT,[5] and RES RRMS is defined as two or more

disabling relapses in the past year, and one or more gadolinium-enhancing lesions on

magnetic resonance imaging (MRI) or increase in T2 lesion load compared with previous

MRI.[5]

With the availability of different disease modifying therapies, there is a need to

understand the relative efficacy of the available treatments in patients with HA or RES

RRMS. Definitions of these RRMS sub-populations were not derived from Phase III trials

but from post hoc sub-group analyses of licensing studies. A number of systematic

literature reviews and network meta-analyses have been published over recent years in

RRMS,[6-8] however, none of them specifically focused on the relative efficacy of

treatment options in HA or RES RRMS patients. There are no published studies with

head-to-head comparisons between all licenced disease modifying therapies in HA and

RES RRMS. It is therefore important to assess whether it is possible to use data from

existing published studies to draw meaningful comparisons between the efficacy of DMTs

in the HA and RES populations, particularly from the perspective of Health Technology

Appraisal (HTA) decision making. Objectives of this study were to conduct a systematic

literature review (SLR) and to assess the feasibility of conducting a Bayesian network

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meta-analysis (NMA) to evaluate the relative efficacy and safety of DMTs in patients with

HA or RES RRMS.

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METHODS

Data sources

A systematic literature review following Preferred Reporting Items for Systematic

Reviews and Meta-analyses (PRISMA) guidelines was performed using a pre-specified

protocol.[9] A previous SLR was conducted in 2010 and this review was an update of

that previous work, focussing on studies post-2010 (data on file). A predefined search

strategy was devised using a combination of medical subject headings (MeSH), Emtree

terms (in Embase) and free text terms for pre-specified interventions in RRMS

(Supplementary Material Tables S1-S3). Searches were conducted in MEDLINE, Embase,

and the Cochrane Library on November 14th, 2014 with no limits on language.

Proceedings of scientific meetings (American Academy of Neurology, European

Committee for Treatment and Research in Multiple Sclerosis) were searched for 2013

and 2014. In addition, the European Medicines Agency, U.S. Food and Drug

Administration, and the ClinicalTrials.gov register were also searched.

Selection of Studies

The records title and abstract was screened by two independent reviewers following

specific PICOS study eligibility criteria (Supplementary Material Table S4). A third

independent reviewer provided consensus when there was disagreement on the inclusion

of the title/abstract of the record. In the cases where exclusion based on the

titles/abstracts was not possible, the full text was retrieved and evaluated. The screening

process was repeated for included full texts using the PICOS criteria for final study

inclusion. Reasons for exclusion were noted in the screening file. The review was

designed to capture RCTs in RRMS, regardless of disease activity, but studies not

reporting in HA or RES RRMS were excluded during screening for the purpose of this

NMA. Only treatments recommended for reimbursement in the UK for RRMS were of

interest in the SLR, with a focus on the HA and RES subgroups. As a result natalizumab

was not deemed to be a comparator of interest in the HA RRMS population because it is

not reimbursed for use in this indication within the NHS in England.[10] Inclusion of

studies in the NMA required the study to include an arm that could form a connection to

one or more other studies in the network.

Data extraction and critical appraisal

Data extraction of studies included was performed by one reviewer and checked by a

second reviewer. Information on study design, selection criteria, study

population/patient characteristics, and interventions were extracted into a data

extraction form, followed by individual study treatment effects and associated

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uncertainty measures for the outcomes of interest. The methodological quality of the

included studies was assessed with the NICE critical assessment checklist,[11] as

adapted from the Centres for Reviews and Dissemination (CRD) checklist for RCTs.[12]

The risk of bias in each individual study was evaluated based on the following items:

adequate method of randomisation, adequate allocation concealment, similarity of

groups, blinding, no unexpected imbalances in drop-out, no selective reporting and

appropriate use of the intention-to-treat principle. The results of the critical appraisal of

included studies are presented in Supplementary Material Table S5.

Network meta-analysis feasibility assessment

The feasibility assessment was performed in three steps: (i) the possibility of

constructing a network of interlinked studies, (ii) study design and patient characteristics

that could modify the relative treatment effect were investigated and (iii) data

availability per outcome of interest was assessed. The efficacy outcomes of interest were

ARR at 12 and 24 months, ARR at any reported time point, difference in change from

baseline EDSS score at 12 or 24 months, difference in change from baseline EDSS score

at any time point, and hazard ratio of 3- and 6-month confirmed disability progression.

Disability progression was selected as one of the key outcomes in this study because it

has driven the health economic modelling of RRMS since the first health economic model

developed in 2003 by the School of Health and Related Research (ScHARR).[13]

To reduce the risk of bias in a NMA, only data from studies with similar study design and

patient populations should be compared. Although some variation in study or patient

characteristics across studies can be expected, an NMA is only valid when no imbalances

exist across comparisons in the study of patient characteristics that can act as effect

modifiers.[14] To assess the feasibility of a valid NMA, the network of interlinked RCTs

was analysed for differences in study design, patient characteristics and outcome

definitions that could potentially bias the relative treatment effects.

The similarity of studies in the HA and RES RRMS populations was assessed, by

evaluating the study design, patient population and outcome definitions of studies

identified in the SLR.[15]

Statistical Analysis

The relative efficacy of the identified interventions for the treatment of HA or RES RRMS

for the selected outcomes was evaluated using a Bayesian NMA. In a Bayesian analysis,

credible intervals (CrI) are used instead of confidence intervals (CI). Credible intervals

assume the true value of the point estimate is within 95% of the range, whereas

confidence intervals assume that if the analysis was replicated 100 times, 95% of the

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confidence intervals would include the true value of the parameter. In this analysis, a

linear model with normal likelihood distribution was used to model 3-month and 6-month

confirmed disability progression at 24 months (as this is a continuous outcome), and a

Poisson likelihood with log link for the annualized relapse rate at 24 months (as this is a

Poisson/rate outcome). For both types of outcomes, a value of hazard ratio or risk ratio

for the intervention versus the control of lower than 1 indicated greater efficacy.

Non-informative prior distributions were assumed for both outcomes. In the presence of

non-informative priors, CrIs can be interpreted similarly to confidence intervals using a

frequentist approach. In addition, if the 95% CrIs do not include 1, results can be

considered statistically significant when using non-informative priors. Prior distributions

of the relative treatment-effects were assumed to be normal, with zero mean and a

variance of 10,000, while a uniform distribution with support from 0 to 5 was used as

prior of the between-study standard deviation.

For each of the outcomes, fixed and random effects models were evaluated and the

better fitting model was selected based on the deviance information criterion (DIC)

which adds a penalty term, equal to the number of effective parameters. Given the small

number of studies included in the analyses (one publication per direct comparison), the

fixed-effects model was chosen over the random effects model. The posterior densities

for unknown parameters were estimated using Markov chain Monte Carlo (MCMC)

simulations. The results presented herein were based on 80,000 iterations on two

chains, with a burn-in of 20,000 iterations. Convergence was assessed by visual

inspection of trace plots. The accuracy of the posterior estimates was assessed using the

Monte Carlo error for each parameter (Monte Carlo error <5% of the posterior standard

deviation). All models were implemented using OpenBUGS version 3.2.2 (MRC

Biostatistics Unit, Cambridge, UK) and Rstudio (R version 3.1.2) and were based on the

models defined by Dias et al.[16]

The Bayesian NMA provided joint posterior distributions of the relative treatment effects

across interventions accompanied with pairwise probabilities of one treatment being

better than another for each of the outcomes. These probabilities were calculated based

on the proportion of MCMC cycles in which a specific treatment estimate was better than

the comparator. Ranking probabilities were also calculated as Surface Under the

Cumulative Ranking Curve (SUCRA); SUCRA is 1 when a treatment is certain to be the

best and 0 when a treatment is certain to be the worst.

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RESULTS

Search and selection results

The searches identified a total of 5,781 records, of which 1,070 were removed as

duplicates. The PRISMA diagram of the screening process is presented in Figure 1. After

merging with the previous SLR from 2010, 8 records were identified that reported data

either for HA RRMS (N=4) and RES RRMS (N=3) or both separately (n=1). Different

subgroup definitions were used across publications, therefore all publications with

borderline HA or RES RRMS patients were thoroughly evaluated using patient

characteristics and any other details in the publications.

Three full text publications, one conference abstract and the EPAR for dimethyl fumarate

(DMF) were identified in the SLR that presented results for HA RRMS patients.[17-21]

The other EPARs did not present subgroup data for HA RRMS. Except for CARE-MS-II, all

studies presented post-hoc subgroup analysis of a RCT or a clinical trial program. It

should be noted that Khatri et al. does not explicitly specify that these patients were

specified to have HA RRMS.[17] The CARE-MS-II study included patients with at least

two attacks in the previous 2 years of which at least one in the previous year, at least

one relapse while on interferon beta or glatiramer after at least 6 months of treatment

and an expanded disability status scale (EDSS) scores of 5.0 or less.[21] This population

was deemed to be borderline HA RRMS, and it was decided to include this study in the

SLR.

In addition, four publications were identified in the SLR for RES RRMS,[19,22-25]

supplemented by a non-published subgroup analysis in RES RRMS which was provided

by the company.[23] Note that one publication presented data for both the HA and RES

RRMS subgroups.[19] Two publications were identified presenting post-hoc subgroup

analyses of the AFFIRM study in a HA RRMS publication.[24,25] The inclusion criteria for

the subgroup were, however, in line with the RES RRMS definition as presented earlier.

Furthermore, Edan et al. included patients with aggressive relapsing multiple sclerosis,

defined as two or more relapses in the last 12 months or EDSS increases by two or more

points (unconfirmed at three months but assessed outside a relapse) and one or more

gadolinium enhancing lesions on MRI.[22] This population definition was deemed similar

enough to RES RRMS to be included in the SLR.

Feasibility of network meta-analysis

Network availability

The first step in assessing the feasibility of a NMA is to examine the evidence base and

to determine whether a network of studies can be constructed. Treatment arms should

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be classified under exclusive categories similar enough to group together in the NMA.

Separate networks were constructed for HA RRMS and RES RRMS, linking studies to each

other through common comparators.

In the HA RRMS network, fingolimod could be linked to DMF using placebo as the

common comparator (Figure 2). Both studies reported ARR and 3-month confirmed

disability progression at 24 months. CARE-MS-II could not be linked to the network for

lacking a common comparator,[21] and TRANSFORMS could not be linked for measuring

outcomes at 12 months instead of 24 months in the other studies.[17] As a result, these

studies could not be included in the NMA. Furthermore, Devonshire 2012 reported

subgroup data of the FREEDOMS study,[19] which was superseded by the results of

Bergvall et al.,[20] whom reported subgroup data for both FREEDOMS and FREEDOMS

II.

For RES RRMS, fingolimod was linked to natalizumab through placebo as the common

comparator (Figure 2). Both studies reported ARR, 3-month confirmed disability

progression and 6-month confirmed disability progression at 24 months. The study by

Edan et al. could not be connected to the network because the study lacked a common

comparator and reports results at 3 months instead of 24 months as reported in the

other studies.[22] As with the HA RRMS network, the publication by Devonshire reported

subgroup data of the FREEDOMS study only and was superseded by the combined

subgroup data of FREEDOMS and FREEDOMS II as provided by the company.[19,23]

Study and patient characteristics

The studies included were all post-hoc subgroup analyses of double blind, parallel group,

multicentre, Phase III, RCTs. The subgroup analysis for natalizumab reported on one

RCT (AFFIRM), whereas fingolimod and DMF were supported by pooled analysis of two

studies (FREEDOMS/FREEDOMS II and DEFINE/CONFIRM respectively).[26-29] The

studies were all conducted over a 24 month duration and the subgroup analyses were

reported at end of study. The subgroup definitions were similar across studies with

regard to treatment experience, relapses and MRI findings (Table 1). Details on the

critical appraisal of studies are presented in Supplementary Material Table S5. Many

items of the risk of bias assessment were not well reported and therefore the risk of bias

of the included subgroup analyses is unclear. Due to the small number of studies in each

network, it was not possible to create funnel plots to assess publication bias.

Patient population characteristics were not always reported for the HA and RES RRMS

subgroups (Supplementary Material Tables S6). The DMF EPAR did not report any patient

characteristics of the subgroup with high disease activity, and the subgroup analysis of

AFFIRM only reported the number of relapses in the year prior to study entry. Although

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no major differences are expected due to the similarity of subgroup definitions, the lack

of reported patient characteristics made it difficult to assess the distribution of potential

effect modifiers in the network.

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Table 1. Key study characteristics for all included studies in the NMA (only arms of interest)

Study Intervention(s) Study design Study

duration

Subgroup definition

Highly active RRMS

Subgroup analysis

of FREEDOMS and

FREEDOMS II[20]

Fingolimod 0.5 mg

OD

Placebo

Post-hoc subgroup

analysis of double-

blind, parallel group,

multicentre RCTs

24 months Patients with high disease activity despite previous DMT use as specified in

one of the following subgroups:

Subgroup 1: those with one or more relapses in the previous year and either

one or more gadolinium (Gd) enhancing T1 lesions or at least nine T2 lesions

at baseline;

Subgroup 2: those with the same number or more relapses in the year

before baseline than in the previous year.

EPAR DMF[18] Dimethyl fumarate

240 mg BID

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCTs

24 months Patients having at least one relapse in the past year while on therapy with

beta-interferon, and at least 9 T2- hyperintense lesions in cranial MRI or at

least 1 Gd-enhancing lesion or having an unchanged or increased relapse

rate.

Rapidly evolving severe RRMS

Subgroup analysis

of FREEDOMS and

FREEDOMS II[23]

Fingolimod 0.5 mg

OD

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCTs

24 months Treatment naïve and at least 2 relapses in year-1 and at least 1 Gd+ lesion

at baseline

Subgroup analysis

of AFFIRM[24,25]

Natalizumab 300 mg

every 4 weeks

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCT

24 months Subgroup of patients that met the criteria for treatment-naïve highly active

relapsing MS (≥2 relapses in the year prior to study entry and ≥1 Gd+ lesion

on T1-weighted MRI at study entry)

BID: twice daily; DMF: Dimethyl fumarate; OD: once daily.

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Network Meta-Analysis results

While limited networks could be constructed for both HA and RES RRMS, the lack of

reported patient characteristics made it difficult to assess the risk of bias in the post-hoc

subgroup analyses and to evaluate the distribution of effect modifiers. Nonetheless, the

subgroup definitions were very similar in terms of required number of relapses, MRI

findings and treatment experience. The subgroup definitions were discussed with a

clinical expert, to validate the inclusion of studies in each network. As a result, a NMA

was deemed feasible but should be interpreted with caution due to the low number of

studies and lack of reported baseline characteristics.

Given the geometry of both networks with a low number of studies and no closed loops

(Figure 2) it was not possible to evaluate whether direct and indirect evidence were in

agreement in closed loops. The individual study results are presented in Table 2.

Table 2. Individual study results for all outcomes of interest in the NMA

Annualized relapse

rate at 24 months

(ARR ratio, 95% CI)

3-month confirmed

disability progression

at 24 months

(HR, 95% CI)

6-month confirmed


at 24 months

(HR, 95% CI)

Highly active RRMS

Subgroup analysis of

FREEDOMS and FREEDOMS II[20]

0.52 (0.40, 0.69) 0.66 (0.45, 0.96) N.A.

EPAR DMF[18] 0.57 (0.39, 0.84) 1.19 (0.66, 2.15) N.A.



FREEDOMS and FREEDOMS II[23]

0.43 (0.25, 0.77) 0.76 (0.30, 1.92) 0.67 (0.22, 2.00)


AFFIRM[24,25]

0.25 (0.16, 0.39) 0.47 (0.24, 0.93) 0.36 (0.17, 0.76)

N.A.: Not applicable, NMA not feasible for this outcome. ARR: annualized relapse rate;

CI: confidence interval; HR: hazard ratio.

Highly Active RRMS

Both active treatments investigated were more efficacious than placebo and

demonstrated lower ARR at 24 months (Figure 3). The results demonstrated no

statistically significant difference in ARR at 24 months between fingolimod 0.5mg OD and

DMF 240mg BID; mean rate ratio 0.91 (95% CrI: 0.57, 1.47). Table 3 presents the

median rank (and 95% CrI), and the probability of being the best treatment and SUCRA

values. Fingolimod 0.5 mg OD has 64.0% probability to be the best treatment, followed

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by DMF 240 mg BID and placebo. The SUCRA values provided identical results regarding

ranking, placing fingolimod 0.5 in first rank (82%) and DMF 240 mg BID in second rank

(67.9%).

Table 3. Estimated ranking of interventions according to different outcomes

Interventions Median rank

(95% CrI)

P

(best) SUCRA (%)

HA RRMS

ARR at 24 months

Fingolimod 0.5 mg OD 1 (1, 2) 64.0% 82.0%

DMF 240 mg BID 2 (1, 2) 36.0% 67.9%

Placebo 3 (3, 3) 0.0% 0.1%

3-month disability progression at 24 months

Natalizumab 300 mg 1 (1, 2) 79.2% 89.0%

Fingolimod 0.5 mg OD 2 (1, 3) 20.4% 46.1%

Placebo 3 (2, 3) 0.4% 14.9%

RES RRMS

ARR at 24 months

Natalizumab 300 mg 1 (1, 2) 93.1% 96.5%

Fingolimod 0.5 mg OD 2 (1, 2) 6.9% 53.4%

Placebo 3 (3, 3) 0.0% 0.1%


Natalizumab 300 mg 1 (1, 2) 79.2% 89.0%

Fingolimod 0.5 mg OD 2 (1, 3) 20.4% 46.1%

Placebo 3 (2, 3) 0.4% 14.9%


Natalizumab 300 mg 1 (1, 2) 81.9% 90.8%

Fingolimod 0.5 mg OD 2 (1, 3) 49.4% 47.0%

Placebo 3 (3, 3) 0.1% 12.2%

While fingolimod was able to demonstrate a statistically significant improvement in 3-

month confirmed disability progression at 24-months over placebo, the difference

between DMF and placebo was not statistically significant (Figure 3). The HA subgroups

were unable to demonstrate statistically significant differences in 3-month confirmed

disability progression for the comparison of fingolimod and DMF. The estimated hazard

ratio was found 0.55 (95% CrI: 0.27, 1.12) in favour or fingolimod. Fingolimod 0.5 mg

OD showed 94.0% probability to be the best treatment and ranked first among dimethyl

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fumarate 240mg BID and placebo in the analysis for three month confirmed disability

progression, with a SUCRA of 96.7%.

Rapidly Evolving Severe RRMS

Both active treatments demonstrated a statistically significant improvement in

annualized relapse rate versus placebo at 24-months. No statistically significant

difference was found for the comparison of fingolimod 0.5 mg OD and natalizumab 300

mg regarding ARR at 24 months; mean rate ratio was estimated to be 1.72 (95% CrI:

0.84, 3.53). All pairwise treatment effects can be found in Figure 4. There was a

significant overlap between therapies with respect to the relative ranking and probability

of being the best across treatments for this outcome.

Similar findings were identified for 3-month confirmed disability progression at 24

months, where the comparison between fingolimod and natalizumab was not deemed

statistically significant (Figure 4). Similarly to annualised relapse rates, there is

significant overlap in the CrI when ranking these therapies. SUCRA values were in

accordance with the results of P(best).

The pattern of results was identical for 6-month confirmed disability progression at 24

months showing no statistically significant difference between fingolimod 0.5 mg OD and

natalizumab 300 mg yet wider credible intervals; hazard ratio of 1.86 (95% CrI: 0.49,

7.12). Again, there were significant overlaps in the median rank CrI between therapies;

however, SUCRA values were quite similar to the probabilities of being the best

treatment for this outcome.

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DISCUSSION

Key findings and implications

In the absence of RCTs comparing all interventions of interest, an NMA is an alternative

to obtain relative efficacy estimates. The evidence identified from the SLR and the

feasibility analysis performed revealed limited data to be synthesised in a NMA for HA

and RES subgroups of interest. Despite the scarcity of data and the lack of information

on patient and study design characteristics, small networks were constructed for each

subgroup, providing analyses for the key outcomes of ARR at 24 months, 3-month

confirmed disability progression at 24 months and 6-month confirmed disability

progression at 24 months (only for the RES RRMS subgroup).

The NMA results regarding the HA subgroup demonstrated no statistically significant

difference between fingolimod and DMF on ARR and disability progression; mean rate

ratio of 0.91 (95% CrI: 0.57, 1.47) and hazard ratio of 0.55 (95% CrI: 0.21, 1.12),

respectively.

For the RES subgroup, no statistically significant difference was found for the comparison

of fingolimod with natalizumab for both ARR and disability progression (3-month and 6-

month confirmed); mean rate ratio of 1.72 (95% CrI: 0.84, 3.52) and hazard ratio of

1.62 (95% CrI: 0.51, 5.13) for 3-month confirmed disability progression and 1.86 (95%

CrI: 0.49, 7.12) for 6-month confirmed disability progression, respectively.

Given the limited evidence base, the results of the analyses should be interpreted with

caution. It should also be noted that all included studies were post-hoc subgroup

analyses of large randomised trials, which were not powered to detect a statistically

significant difference between interventions in the HA or RES RRMS subgroups.

Strengths and limitations

The first strength of this analysis was the comprehensive and robust search strategy

which was developed to identify all relevant interventions for the treatment of RRMS.

The evidence base was subsequently tailored to HA and RES subgroups, causing

potential bias to the analyses performed since the baseline characteristics of the included

studies could not be adequately evaluated. Therefore, a thorough assessment of the

similarity of subgroup definitions was performed prior to the full analysis. Not all

publications used the same definition of HA and RES RRMS, therefore all studies with

borderline HA or RES RRMS populations or subgroups were reviewed by a clinical expert.

Studies were only synthesised in the NMA if the subgroup definitions were similar.

Although it is possible to adjust for potential treatment effect modifiers by performing

meta-regression or sensitivity analysis excluding studies with differences in effect

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modifiers, the limited evidence base in the current analysis did not allow for such

analyses. Furthermore, studies were only synthesized if the patient populations were

similar, i.e. using the same definitions for HA and RES RRMS. This limited the impact of

potential imbalances on the results of the NMA. Although the studies were deemed

similar enough to be synthesised in an NMA, residual confounding may still exist in the

aggregated data. The results of the NMA for the HA RRMS population were the same as

those reported by the manufacturer in the 2014 Scottish Medicines Consortium (SMC)

submission for fingolimod,[30] indicating that new data for these subgroups has not

been published recently.

Conclusion

The lack of data and resulting high level of uncertainty around the NMA estimates of

comparative treatment effectiveness for patients with HA or RES RRMS provides a

challenge to Health Technology Assessment groups appraising the evidence. An NMA can

offer point estimates for inclusion in economic models but these estimates will be

associated with high levels of uncertainty which would be further compounded if

considered as a basis for HTA decision making. Until there is a major change in the

available data for the treatments used in these indications, such as additional studies of

the DMTs of interest in HA and RES RRMS, it will be difficult for HTA assessment groups

to make reimbursement decisions on behalf of patients with HA and RES RRMS and the

healthcare professionals who support them.

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Acknowledgements

The authors thank Julien Gagnon, also an employee of Mapi, for helping with the

submission process.

Contributors

The study design and protocol were developed by ZF, JJ, and MB in collaboration with FA

and NA. ZF, JJ, MB, FA, and NA collected the data and conducted the SLR. EH and KP

performed the feasibility study and the network meta-analysis. The manuscript was

written by EH, JJ, MB, and NA. All authors have been involved in reviewing the study

outcomes and have approved the final version of the manuscript.

Funding The study and manuscript were funded by Novartis.

Competing interests

FA and NA are employees of Novartis. EH, KP, ZF, JJ and MB are employees of Mapi, and

served as paid consultants to Novartis to conduct the systematic literature review and

preparation of this manuscript. All authors have been involved in the review of the

systematic literature review, the model results, and the manuscript.

Data sharing statement No additional data are available.

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Multiple Sclerosis (TRANSFORMS). Multiple Sclerosis and Related Disorders

2014;3(3):355-63

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24. Hutchinson M, Kappos L, Calabresi PA, et al. The efficacy of natalizumab in patients

with relapsing multiple sclerosis: subgroup analyses of AFFIRM and SENTINEL.

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25. Kappos L, O'Connor PW, Polman CH, et al. Clinical effects of natalizumab on multiple

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2013;260(5):1388-95

26. Calabresi PA, Radue EW, Goodin D, et al. Safety and efficacy of fingolimod in

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27. Kappos L, Radue EW, O'Connor P, et al. A placebo-controlled trial of oral fingolimod

in relapsing multiple sclerosis. The New England journal of medicine

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28. Gold R, Kappos L, Arnold DL, et al. Placebo-controlled phase 3 study of oral BG-12

for relapsing multiple sclerosis. The New England journal of medicine

2012;367(12):1098-107

29. Fox RJ, Miller DH, Phillips JT, et al. Placebo-controlled phase 3 study of oral BG-12 or

glatiramer in multiple sclerosis. The New England journal of medicine

2012;367(12):1087-97

30. Scottish Medicines Consortium. fingolimod, 0.5mg, hard capsules (Gilenya) SMC No

(992/14). 2014

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Figure 1. Study identification flow diagram Figure 1

297x203mm (96 x 96 DPI)

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Figure 2. Feasible networks for (a) HA RRMS; (b) RES RRMS Figure 2

193x92mm (96 x 96 DPI)

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Figure 3. NMA results for fingolimod versus dimethyl fumarate in HA RRMS Figure 3

247x94mm (150 x 150 DPI)

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Figure 4. NMA results fingolimod versus natalizumab in RES RRMS Figure 4

353x115mm (150 x 150 DPI)

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Table S1: Search strategy for the systematic literature review - Medline search

string (November 14, 2014)

# Search terms Hits

1 Multiple Sclerosis/ OR Multiple Sclerosis, Relapsing-Remitting/ OR Myelitis,

Transverse/ OR Demyelinating Diseases/ OR Encephalomyelitis, Acute

Disseminated/

55747

2 ("multiple sclerosis" OR "transverse myelitis" OR "optic neuritis" OR "devic"

OR "adem" OR "neuromyelitis optica").tw.

56922

3 1 OR 2 72672

4 (Fingolimod OR Gilenya OR FTY720 OR FTY-720 OR FTY 720 OR fingolimod

hydrochloride OR 2-amino-2-2-4-octylphenylethyl-1,3-propanediol

hydrochloride).tw.

1601

5 exp Interferon-beta/ or (Interferon beta* or IFN-beta or rebif* or avonex or betaseron or Fibroblast IFN or Interferon-beta* or betaferon or IFN beta

precursor or IFN beta* or IFNbeta* or Extavia).tw.

12828

6 (Glatiramer acetate OR Copaxone OR Copolymer1 OR copolymer 1 OR Co

polymer1 OR Co polymer 1 OR COP1 OR COP 1 OR TV 5010 OR TV-5010

OR TV5010).tw.

1695

7 (Cladribine OR Leustat OR Leustatin OR 2CDA OR Mylinax OR Movectro OR

2 chloro 2 deoxyadenosine OR 2chloro2deoxyadenosine OR 2chloro

2deoxyadenosine OR 2chloro2deoxybetaadenosine OR 2 chloro 2 deoxy

beta adenosine OR 2 chloro 2 deoxybetaadenosine OR 2chloro

2deoxybetaadenosine OR 2 Chlorodeoxyadenosine OR

2Chlorodeoxyadenosine OR Chloroadenosine).tw.

2660

8 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4

integrin OR Anti-alpha4integrin).tw.

1333

9 (Best Supportive Care or Best Available Care).tw. 1281

10 (Teriflunomide OR HMR1726 OR Flucyamide OR Aubagio OR A77 1726 OR SU 20 OR A 771726 OR HMR 1726 OR HMR-1726 OR UNII-1C058IKG3B

OR A771726).tw.

331

11 (Dimethyl Fumarate OR Dimethylfumarate OR 624-49-7 OR Fumaric Acid

OR Dimethyl Ester OR Fumaderm OR Methyl Fumarate OR 2-Butenedioic

Acid OR Dimethyl Ester OR Tecfidera OR FAG 201 OR FAG201 OR BG12 OR

BG-12 OR BG00012 OR BG-00012).tw.

1572

12 (Alemtuzumab OR MabCambath OR Campath OR Lemtrada OR UNII-

3A189DH42V OR 126775-97-1 OR 216503-57-0 OR 478159-77-2 OR

727728-72-5).tw.

2232

13 OR/4-12 24039

14 (clinical trial or Controlled Clinical Trial).pt. or exp Randomized Controlled

Trials as Topic/ or (Randomised Clinical Trial or Randomised Clinical Trials

or Randomized Clinical Trial or Randomized Clinical Trials or Randomised

Controlled Trial or Randomized Controlled Trials or Randomized Controlled

Trial or Randomized Controlled Trials or Randomised Trial or Randomised

Trials or Randomized Trial or Randomized Trials or Random Allocation or Double Blind Method or Single Blind Method or Placebo* or Allocated

Random* or Open-label Trial* or Open-label Stud* or Non-blinded

Stud*).tw. or exp Cohort Publications/ or (Cohort Analyses or Cohort Analysis or Longitudinal).tw. or exp Follow-Up Publications/ or evaluation

stud*.tw. or exp Prospective Publications/ or Observational.tw.

2201588

15 3 and 13 and 14 1975

16 animals/ not humans/ 4004891

17 15 not 16 1968

18 limit 17 to yr="2010 -Current" 788

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Table S2: Search strategy for the systematic literature review - Embase search


# Search terms Hits

1 multiple sclerosis/ OR myelitis/ OR demyelinating disease/ OR postvaccinal

encephalitis/

80634

2 ("multiple sclerosis" OR "transverse myelitis" OR "optic neuritis" OR "devic"

OR "adem" OR "neuromyelitis optica").tw.

66562

3 1 OR 2 88993

4 (Fingolimod OR Gilenya OR FTY720 OR FTY-720 OR FTY 720 OR fingolimod hydrochloride OR 2-amino-2-2-4-octylphenylethyl-1,3-propanediol

hydrochloride).tw.

3673

5 exp Interferon-beta/ or (Interferon beta* or IFN-beta or rebif* or avonex or betaseron or Fibroblast IFN or Interferon-beta* or betaferon or IFN beta

precursor or IFN beta* or IFNbeta* or Extavia).tw.

23572


polymer1 OR Co polymer 1 OR COP1 OR COP 1 OR TV 5010 OR TV-5010

OR TV5010).tw.

3557

7 (Cladribine OR Leustat OR Leustatin OR 2CDA OR Mylinax OR Movectro OR

2 chloro 2 deoxyadenosine OR 2chloro2deoxyadenosine OR 2chloro

2deoxyadenosine OR 2chloro2deoxybetaadenosine OR 2 chloro 2 deoxy beta adenosine OR 2 chloro 2 deoxybetaadenosine OR 2chloro

2deoxybetaadenosine OR 2 Chlorodeoxyadenosine OR


2945


integrin OR Anti-alpha4integrin).tw.

3680


10 (Teriflunomide OR HMR1726 OR Flucyamide OR Aubagio OR A77 1726 OR SU 20 OR A 771726 OR HMR 1726 OR HMR-1726 OR UNII-1C058IKG3B OR

A771726).tw.

789



Acid OR Dimethyl Ester OR Tecfidera OR FAG 201 OR FAG201 OR BG12 OR BG-12 OR BG00012 OR BG-00012).tw.

2254

12 (Alemtuzumab or MabCambath or Campath or Lemtrada or UNII-

3A189DH42V or 126775-97-1 or 216503-57-0 or 478159-77-2 or 727728-72-5).tw.

10699

13 OR/4-12 47452

14 (Clinical trial OR Controlled Clinical Trial).pt. OR exp Randomized Controlled

Trials as Topic/ OR (Randomised Clinical Trial OR Randomised Clinical Trials

OR Randomized Clinical Trial OR Randomized Clinical Trials OR Randomised Controlled Trial OR Randomized Controlled Trials OR Randomized

Controlled Trial OR Randomized Controlled Trials OR Randomised Trial OR

Randomised Trials OR Randomized Trial OR Randomized Trials OR Random Allocation OR Double Blind Method OR Single Blind Method OR Placebo* OR

Allocated Random* OR Open-label Trial* OR Open-label Stud* OR Non-

blinded Stud*).tw. OR Exp Cohort Publications/ OR (Cohort Analyses OR Cohort Analysis OR Longitudinal).tw. OR Exp Follow-Up Publications/ OR

(evaluation stud*).tw OR exp Prospective Publications/ OR

Observational.tw.

1668075

15 3 AND 13 AND 14 3672


17 15 not 16 3672


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Table S3: Search strategy for the systematic literature review - Cochrane

Search String (November 14, 2014)

# Search Terms Hits

1 MeSH DESCRIPTOR [Multiple Sclerosis] explode all trees 1861

2 MeSH descriptor: [Myelitis, Transverse] explode all trees 11

3 MeSH descriptor: [Demyelinating Diseases] explode all trees 2077

4 MeSH descriptor: [Encephalomyelitis, Acute Disseminated] explode all

trees

3

5 MeSH descriptor: [Multiple Sclerosis, Relapsing-Remitting] explode all

trees

417

6 ("multiple sclerosis" or "transverse myelitis" or "optic neuritis" or "devic" or "adem" or "neuromyelitis optica")

4214

7 #1 or #2 or #3 or #4 or #5 or #6 4418

8 (Fingolimod OR Gilenya OR FTY720 OR FTY-720 OR FTY 720 OR

fingolimod hydrochloride OR 2-amino-2-(2-(4-octylphenyl)ethyl)-1,3-propanediol hydrochloride)

158

9 MeSH descriptor: [Interferon-beta] explode all trees 520

10 (Interferon beta* or IFN-beta or rebif* or avonex or betaseron or

Fibroblast IFN or Interferon-beta* or betaferon or IFN beta precursor or

IFN beta* or IFNbeta* or Extavia)

1526

11 (Glatiramer acetate OR Copaxone OR Copolymer1 OR copolymer 1 OR

Co polymer1 OR Co polymer 1 OR COP1 OR COP 1 OR TV 5010 OR TV-

5010 OR TV5010)

805

12 (Cladribine OR Leustat OR Leustatin OR 2CDA OR Mylinax OR Movectro

OR 2 chloro 2 deoxyadenosine OR 2chloro2deoxyadenosine OR 2chloro 2deoxyadenosine OR 2chloro2deoxybetaadenosine OR 2 chloro 2 deoxy

beta adenosine OR 2 chloro 2 deoxybetaadenosine OR 2chloro

2deoxybetaadenosine OR 2 Chlorodeoxyadenosine OR 2Chlorodeoxyadenosine OR Chloroadenosine)

192


integrin OR Anti-alpha4integrin)

156

14 (Teriflunomide OR HMR1726 OR Flucyamide OR Aubagio OR A77 1726

OR SU 20 OR A 771726 OR HMR 1726 OR HMR-1726 OR UNII-1C058IKG3B OR A771726)

3149

15 (Dimethyl Fumarate OR Dimethylfumarate OR 624-49-7 OR Fumaric Acid OR Dimethyl Ester OR Fumaderm OR Methyl Fumarate OR 2-Butenedioic

Acid OR Dimethyl Ester OR Tecfidera OR FAG 201 OR FAG201 OR BG12

OR BG-12 OR BG00012 OR BG-00012)

143

16 Alemtuzumab OR MabCambath OR Campath OR Lemtrada OR UNII-

3A189DH42V OR 126775-97-1 OR 216503-57-0 OR 478159-77-2 OR

727728-72-5

285

17 #8 or #9 or #10 or #11 or #12 or #13 or #14 or #15 or #16 6018

18 #7 and #17 [Publication Year from 2010, in Trials and Reviews] 353

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Table S4: Patients, interventions, comparisons, outcomes and study design

(PICOS) criteria for inclusion in the SLR

Criteria Inclusion

Patient population

Abstract

screening

Adults with all RRMS including:

• Active RRMS

• HA RRMS*

• RES RRMS*

Full text

screening Adults with HA RRMS or RES RRMS*

Intervention

Abstract

screening

• Fingolimod

• Beta interferon

• Glatiramer acetate

• Natalizumab

• Teriflunomide

• Dimethyl fumarate

• Alemtuzumab

Full text

screening

Licenced treatments in HA RRMS:

• Fingolimod

• Beta interferon

• Glatiramer acetate

• Teriflunomide

• Dimethyl fumarate

• Alemtuzumab

Licenced treatments in RES RRMS:

• Fingolimod

• Natalizumab

Comparison (any

dosage)

Abstract

and full

text

screening

Any of the interventions above or best supportive care

Outcomes

Abstract

and full

text

screening

Functional Outcomes

• Annualized relapse rate (ARR)

• ARR ratio

• Hazard ratio (HR) for time to relapse

• HR for disability progression (at 3 and 6 months

or otherwise)

• Proportion of patients with no relapses

• Mean change from baseline in EDSS score

• Proportion of patients disease activity free

• Proportion of patients with no change in EDSS

MRI Outcomes

• Mean number of new or enlarged T2 hyper

intense lesions

• Proportion of patients with no T2 lesions

• Mean MS Functional composite scale z-score

Study design

Abstract

and full

text

screening

Randomised controlled trials

* Highly active RRMS was defined as having an unchanged or increased relapse rate or on-going severe relapses compared with the previous year despite treatment with a DMT, RES RRMS was defined as two or more disabling relapses in the past year, and one or more gadolinium-enhancing lesions on MRI or increase in T2 lesion load compared with previous MRI. ARR: Annualised relapse rate; HR: Hazard ratio; EDSS: Expanded

disability status scale; MRI: Magnetic Resonance Imaging.

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Table S5: Risk of bias assessment of included studies

Study Was the

method used

to generate

random

allocations

adequate?

Was the

allocation

adequately

concealed?

Were the

groups

similar at the

outset of the

study terms

of prognostic

factors, for

example,

severity?

Were the

care

providers,

participants

and outcome

assessors

blind to

treatment

allocation?

Were there

any

unexpected

imbalances

in drop-outs

between

groups?

Is there any

evidence to

suggest that

the authors

measured

more

outcomes

than they

reported?

Did the analysis

include an

intention-to-treat

analysis? If so,

what was the

appropriate

method used to

account for missing

data?

Highly active RRMS

Subgroup analysis

of FREEDOMS and

FREEDOMS II[1]

Yes Yes Yes Yes Unclear Unclear Unclear

EPAR DMF[2] Unclear Unclear Unclear Yes Unclear Unclear Unclear


Subgroup analysis

of FREEDOMS and

FREEDOMS II[3]

Unclear Unclear Unclear Yes Unclear Unclear Unclear

Subgroup analysis

of AFFIRM[4,5]


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Table S6. Key patient characteristics at baseline and study results for all included studies in the NMA (only arms of

interest)

Author and

year

Treatment Number of

patients

% female Mean age

(years)

Mean

duration of

MS since

diagnosis

(years)

Mean

number of

relapses in

past year

Mean

baseline

EDSS score

Mean

number of

GAD

enhancing

lesions

Highly active RRMS

Subgroup

analysis of

FREEDOMS and

FREEDOMS II[1]

Fingolimod 0.5mg OD 249 76.3% 39.3 6.3 1.5 2.5 1.9

Placebo 257 74.7% 39.2 6.2 1.6 2.7 1.3

EPAR DMF[2] DMF 240 mg BID NR NR NR NR NR NR NR

Placebo NR NR NR NR NR NR NR

RES RRMS

Subgroup

analysis of

FREEDOMS and

FREEDOMS II[3]

Fingolimod 0.5mg OD 59 72.9% 32.4 1.8 2.4 2.3 NR

Placebo 47 66.0% 32.8 3.7 2.3 2.0 NR

Subgroup

analysis of

AFFIRM[4,5]

Natalizumab 300 mg

every 4 weeks

148 NR NR NR 2.45 NR NR

Placebo 61 NR NR NR 2.28 NR NR


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References




2. European Medicines Agency. European public assessment reports Tecfidera (Common

Name: dimethyl fumarate). 2013.



3. Novartis. Subgroup analysis of treatment naive patients who had at least 2 relapses in

the past year and at least 1 Gd+ T1 lesion at baseline. Data on file






2013;260(5):1388-95

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PRISMA NMA Checklist of Items to Include When Reporting A Systematic Review

Involving a Network Meta-analysis

Section/Topic Item

#

Checklist Item Reported on Page

#

TITLE

Title 1 Identify the report as a systematic review

incorporating a network meta-analysis (or

related form of meta-analysis).

Page 1

ABSTRACT

Structured

summary

2 Provide a structured summary including,

as applicable:

Background: main objectives

Methods: data sources; study eligibility

criteria, participants, and interventions;

study appraisal; and synthesis methods,

such as network meta-analysis.

Results: number of studies and

participants identified; summary estimates

with corresponding confidence/credible

intervals; treatment rankings may also be

discussed. Authors may choose to

summarize pairwise comparisons against a

chosen treatment included in their

analyses for brevity.

Discussion/Conclusions: limitations;

conclusions and implications of findings.

Other: primary source of funding;

systematic review registration number

with registry name.

Page 2

INTRODUCTION

Rationale 3 Describe the rationale for the review in the Pages 4/5 – end of

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context of what is already known,

including mention of why a network meta-

analysis has been conducted.

introduction

Objectives 4 Provide an explicit statement of questions

being addressed, with reference to

participants, interventions, comparisons,

outcomes, and study design (PICOS).

Pages 4/5 – end of

introduction

METHODS

Protocol and

registration

5 Indicate whether a review protocol exists

and if and where it can be accessed (e.g.,

Web address); and, if available, provide

registration information, including

registration number.

Page 6 – Data

sources

Eligibility criteria 6 Specify study characteristics (e.g., PICOS,

length of follow-up) and report

characteristics (e.g., years considered,

language, publication status) used as

criteria for eligibility, giving rationale.

Clearly describe eligible treatments

included in the treatment network, and

note whether any have been clustered or

merged into the same node (with

justification).

Supplementary

Material – Table S4

Information

sources

7 Describe all information sources (e.g.,

databases with dates of coverage, contact

with study authors to identify additional

studies) in the search and date last

searched.

Page 6 – Data

sources

Search 8 Present full electronic search strategy for

at least one database, including any limits

used, such that it could be repeated.

Supplementary

Material – Table

S1-S3

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Study selection 9 State the process for selecting studies

(i.e., screening, eligibility, included in

systematic review, and, if applicable,

included in the meta-analysis).

Page 6 – Selection

of studies

Data collection

process

10 Describe method of data extraction from

reports (e.g., piloted forms,

independently, in duplicate) and any

processes for obtaining and confirming

data from investigators.

Pages 6/7 – Data

extraction and

quality assessment

Data items 11 List and define all variables for which data

were sought (e.g., PICOS, funding

sources) and any assumptions and

simplifications made.

Page 7 –

Assessment of

network meta-

analysis feasibility

Geometry of

the network

S1 Describe methods used to explore the

geometry of the treatment network under

study and potential biases related to it.

This should include how the evidence base

has been graphically summarised for

presentation, and what characteristics

were compiled and used to describe the

evidence base to readers.

Page 7 –

Assessment of

network meta-


Risk of bias

within individual

studies

12 Describe methods used for assessing risk

of bias of individual studies (including

specification of whether this was done at

the study or outcome level), and how this

information is to be used in any data

synthesis.

Page 7 – Data

extraction and

quality assessment

Summary

measures

13 State the principal summary measures

(e.g., risk ratio, difference in means). Also

describe the use of additional summary

measures assessed, such as treatment

rankings and surface under the cumulative

ranking curve (SUCRA) values, as well as

Pages 7/8 –

Assessment of

network meta-

analysis

feasibility/Statistical

analysis

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modified approaches used to present

summary findings from meta-analyses.

Planned methods

of analysis

14 Describe the methods of handling data

and combining results of studies for each

network meta-analysis. This should

include, but not be limited to:

• Handling of multi-arm trials;

• Selection of variance structure;

• Selection of prior distributions in

Bayesian analyses; and

• Assessment of model fit.

Pages 7/8 –

Statistical analysis

Assessment of

Inconsistency

S2 Describe the statistical methods used to

evaluate the agreement of direct and

indirect evidence in the treatment

network(s) studied. Describe efforts taken

to address its presence when found.

Not applicable, no

closed loops

Risk of bias

across studies

15 Specify any assessment of risk of bias that

may affect the cumulative evidence (e.g.,

publication bias, selective reporting within

studies).

Pages 9/10 –

Feasibility of

network meta-

analysis

Additional

analyses

16 Describe methods of additional analyses if

done, indicating which were pre-specified.

This may include, but not be limited to,

the following:

• Sensitivity or subgroup analyses;

• Meta-regression analyses;

• Alternative formulations of the

treatment network; and

• Use of alternative prior

distributions for Bayesian analyses

(if applicable).

Not applicable, not

enough studies, see

pages 16/17 –

Discussion

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RESULTS†

Study selection 17 Give numbers of studies screened,

assessed for eligibility, and included in the

review, with reasons for exclusions at

each stage, ideally with a flow diagram.

Figure 1

Page 9 – Search

and selection

results

Presentation of

network

structure

S3 Provide a network graph of the included

studies to enable visualisation of the

geometry of the treatment network.

Figure 2

Summary of

network

geometry

S4 Provide a brief overview of characteristics

of the treatment network. This may

include commentary on the abundance of

trials and randomised patients for the

different interventions and pairwise

comparisons in the network, gaps of

evidence in the treatment network, and

potential biases reflected by the network

structure.

Pages 9-11 –


Study

characteristics

18 For each study, present characteristics for

which data were extracted (e.g., study

size, PICOS, follow-up period) and provide

the citations.

Supplementary


Risk of bias

within studies

19 Present data on risk of bias of each study

and, if available, any outcome level

assessment.

Supplementary


Results of

individual studies

20 For all outcomes considered (benefits or

harms), present, for each study: 1) simple

summary data for each intervention

group, and 2) effect estimates and

confidence intervals. Modified approaches

may be needed to deal with information

from larger networks.

Table 2

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Synthesis of

results

21 Present results of each meta-analysis

done, including confidence/credible

intervals. In larger networks, authors may

focus on comparisons versus a particular

comparator (e.g. placebo or standard

care), with full findings presented in an

appendix. League tables and forest plots

may be considered to summarisze

pairwise comparisons. If additional

summary measures were explored (such

as treatment rankings), these should also

be presented.

Results presented

per subgroup and

per outcome of

interest

Exploration for

inconsistency

S5 Describe results from investigations of

inconsistency. This may include such

information as measures of model fit to

compare consistency and inconsistency

models, P values from statistical tests, or

summary of inconsistency estimates from

different parts of the treatment network.

Not applicable, no

closed loops

Risk of bias

across studies

22 Present results of any assessment of risk

of bias across studies for the evidence

base being studied.

Page 13 – Network

Meta-Analysis

results

Results of

additional

analyses

23 Give results of additional analyses, if done

(e.g., sensitivity or subgroup analyses,

meta-regression analyses, alternative

network geometries studied, alternative

choice of prior distributions for Bayesian

analyses, and so forth).

Not applicable, not

enough studies, see

page 16 –

Discussion

DISCUSSION

Summary of

evidence

24 Summarise the main findings, including

the strength of evidence for each main

outcome; consider their relevance to key

groups (e.g., healthcare providers, users,

Pages 16/17 – Key

findings and

implications

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and policy-makers).

Limitations 25 Discuss limitations at study and outcome

level (e.g., risk of bias), and at review

level (e.g., incomplete retrieval of

identified research, reporting bias).

Comment on the validity of the

assumptions, such as transitivity and

consistency. Comment on any concerns

regarding network geometry (e.g.,

avoidance of certain comparisons).

Pages 16/17 –

Strengths and

limitations

Conclusions 26 Provide a general interpretation of the

results in the context of other evidence,

and implications for future research.

Page 17 -

Conclusion

FUNDING

Funding 27 Describe sources of funding for the

systematic review and other support (e.g.,

supply of data); role of funders for the

systematic review. This should also

include information regarding whether

funding has been received from

manufacturers of treatments in the

network and/or whether some of the

authors are content experts with

professional conflicts of interest that could

affect use of treatments in the network.

Page 18 -

Disclosure

PICOS = population, intervention, comparators, outcomes, study design.

* Text in italics indicates wording specific to reporting of network meta-analyses that has

been added to guidance from the PRISMA statement.

† Authors may wish to plan for use of appendices to present all relevant information in full

detail for items in this section.

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A systematic literature review and network meta-analysis in

highly active relapsing remitting multiple sclerosis and

rapidly evolving severe multiple sclerosis.

Journal: BMJ Open

Manuscript ID bmjopen-2016-013430.R1

Article Type: Research

Date Submitted by the Author: 08-Nov-2016

Complete List of Authors: Huisman, Eline; Mapi Group Papadimitropoulou, Katerina; Mapi Group Jarrett, James; Mapi Group

Bending, Matthew; Mapi Group Firth, Zoe; Mapi Group Allen, Felicity; Novartis Pharmaceuticals UK Ltd Adlard, Nick; Novartis Pharmaceuticals UK Ltd

<b>Primary Subject Heading</b>:

Pharmacology and therapeutics

Secondary Subject Heading: Qualitative research, Neurology, Health economics

Keywords: Fingolimod, Natalizumab, Dimethyl fumarate, Network meta-analysis, RRMS


BMJ Open on M

ay 15, 2021 by guest. Protected by copyright.

http://bmjopen.bm

j.com/

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J Open: first published as 10.1136/bm

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Page 1 of 22

A systematic literature review and network meta-analysis in highly active

relapsing remitting multiple sclerosis and rapidly evolving severe multiple

sclerosis.

Eline Huisman1, Katerina Papadimitropoulou1, James Jarrett2, Matthew Bending2, Zoe

Firth2, Felicity Allen3, Nick Adlard3

1Mapi Group, De Molen 84, 3995 AX Houten, Netherlands

2Mapi Group, 73 Collier Street, London N1 9EB, UK

3Novartis Pharmaceuticals, Frimley Business Park, Frimley, Camberley, Surrey GU16

7SR, UK

Corresponding author contact details:

Nick Adlard

Novartis Pharmaceuticals UK Limited

200 Frimley Business Park

Frimley, Camberley, Surrey GU16 7SR

UNITED KINGDOM

Phone: +44 7741 292 178 [email protected]

Keywords: Fingolimod, natalizumab, dimethyl fumarate, network meta-analysis, RRMS

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Page 2 of 22

ABSTRACT

Objective: Multiple sclerosis (MS) is a chronic, neurodegenerative autoimmune disorder

affecting the central nervous system. Relapsing Remitting MS (RRMS) is the most

common clinical form of MS and affects approximately 85% of cases at onset. Highly

active (HA) and rapidly evolving severe (RES) RRMS are two forms of RRMS amenable to

disease modifying therapies (DMT). This study explored the efficacy of fingolimod

relative to other DMTs for the treatment of HA and RES RRMS.

Methods: A systematic literature review (SLR) was conducted to identify published

randomised controlled trials (RCTs) in HA and RES RRMS. Identified evidence was

vetted, and a Bayesian network meta-analysis (NMA) was performed to evaluate the

relative efficacy of fingolimod versus dimethyl fumarate (DMF) in HA RRMS and versus

natalizumab in RES RRMS.

Results: For HA RRMS, the SLR identified two studies with relevant patient subgroup

data: one comparing fingolimod with placebo and the other comparing DMF with

placebo. Three studies were found for RES RRMS: one comparing fingolimod with

placebo and two studies comparing natalizumab with placebo. NMA results in the HA

population showed a favourable numerical trend of fingolimod vs. DMF assessed for

annualized relapse rate (ARR) and three-month confirmed disability progression. For the

RES population, the results identified an increase of ARR and 3-month confirmed

disability progression for fingolimod vs. natalizumab (not statistically significant). Sparse

study data and the consequently high uncertainty around the estimates restricted our

ability to demonstration statistical significance in the studied subgroups.

Conclusion: Data limitations are apparent when conducting an informative indirect

comparison for the HA and RES RRMS subgroups as the subgroups analyses were

retrospective analyses of studies powered to indicate differences across entire study

populations. Comparisons across treatments in HA or RES RRMS will be associated with

high levels of uncertainty until new data is collected for these subgroups.

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Page 3 of 22

STRENGTHS AND LIMITATIONS OF THIS STUDY

• Comprehensive and robust search strategy developed to identify all relevant

interventions for the treatment of RRMS.

• Potential bias in the analyses since the baseline characteristics of the HA and RES

subgroups could not be adequately evaluated in some studies

• Accordingly, studies were only synthesised if the patient populations used the

same definitions for HA and RES RRMS, which limited the impact of potential

imbalances on NMA results.

• The limited evidence base prohibited adjustments for potential treatment effect

modifiers.

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Page 4 of 22

INTRODUCTION

Multiple sclerosis (MS) is a disease of the central nervous system where myelin within

the brain or spinal cord becomes inflamed and is then destroyed by the immune

system.1 It can be classified into three subtypes: relapsing and remitting MS (RRMS),

secondary progressive MS and primary progressive MS. RRMS is the most common

clinical form of MS and accounts for approximately 85% of cases at onset.2 In RRMS,

people have distinct attacks of symptoms which then fade away either partially or

completely. Symptoms may not all be experienced at the same time but can include

visual disturbance, lack of balance and dizziness, chronic fatigue, bladder problems,

pain, muscle weakness or spasticity and cognitive impairment.3

Although there is still no cure for MS, research has shown major improvements in MS

treatment over the last 20 years and multiple disease modifying treatments (DMT) have

become available since then, including interferon beta, glatiramer acetate, teriflunomide,

dimethyl fumarate, natalizumab, fingolimod, and alemtuzumab.1 4 Many of these

treatments focus on early phases of the disease, fewer treatment options are available

for patients with highly active (HA) or rapidly evolving severe (RES) RRMS. Data on

populations of patients with HA and RES RRMS are the subject of the analysis. At the

time of this study, HA RRMS was defined in the fingolimod label as an unchanged or

increased relapse rate or on-going severe relapses compared with the previous year

despite treatment with at least one DMT,5 and RES RRMS is defined as two or more

disabling relapses in the past year, and one or more gadolinium-enhancing lesions on

magnetic resonance imaging (MRI) or increase in T2 lesion load compared with previous

MRI.5

With the availability of different disease modifying therapies, there is a need to

understand the relative efficacy of the available treatments in patients with HA or RES

RRMS. Definitions of these RRMS sub-populations were not derived from Phase III trials

but from post hoc sub-group analyses of licensing studies. A number of systematic

literature reviews and network meta-analyses have been published over recent years in

RRMS,6-8 however, none of them specifically focused on the relative efficacy of treatment

options in HA or RES RRMS patients. There are no published studies with head-to-head

comparisons between all licenced disease modifying therapies in HA and RES RRMS. It is

therefore important to assess whether it is possible to use data from existing published

studies to draw meaningful comparisons between the efficacy of DMTs in the HA and RES

populations, particularly from the perspective of Health Technology Appraisal (HTA)

decision making. Objectives of this study were to conduct a systematic literature review

(SLR) and to assess the feasibility of conducting a Bayesian network meta-analysis

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Page 5 of 22

(NMA) to evaluate the relative efficacy and safety of DMTs in patients with HA or RES

RRMS.

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METHODS

Data sources

A systematic literature review following Preferred Reporting Items for Systematic

Reviews and Meta-analyses (PRISMA) guidelines was performed using a pre-specified

protocol.9 A previous SLR was conducted in 2010 and this review was an update of that

previous work, focussing on studies post-2010 (data on file). A predefined search

strategy was devised using a combination of medical subject headings (MeSH), Emtree

terms (in Embase) and free text terms for pre-specified interventions in RRMS

(Supplementary Material Tables S1-S3). Searches were conducted in MEDLINE, Embase,

and the Cochrane Library on November 14th, 2014 with no limits on language.

Proceedings of scientific meetings (American Academy of Neurology, European

Committee for Treatment and Research in Multiple Sclerosis) were searched for 2013

and 2014. In addition, the European Medicines Agency, U.S. Food and Drug

Administration, and the ClinicalTrials.gov register were also searched.

Selection of Studies

The records title and abstract were screened by two independent reviewers following

specific PICOS study eligibility criteria (Supplementary Material Table S4). A third

independent reviewer provided consensus when there was disagreement on the inclusion

of the title/abstract of the record. In the cases where exclusion based on the

titles/abstracts was not possible, the full text was retrieved and evaluated. The screening

process was repeated for included full texts using the PICOS criteria for final study

inclusion. Reasons for exclusion were noted in the screening file. The review was

designed to capture RCTs in RRMS, regardless of disease activity, but studies not

reporting in HA or RES RRMS were excluded during screening for the purpose of this

NMA. Only treatments recommended for reimbursement in the UK for RRMS were of

interest in the SLR, with a focus on the HA and RES subgroups. As a result natalizumab

was not deemed to be a comparator of interest in the HA RRMS population because it is

not reimbursed for use in this indication within the NHS in England.10 It should be noted

that no restrictions were placed on the trial location of the included studies. Inclusion of

studies in the NMA required the study to include an arm that could form a connection to

one or more other studies in the network.

Data extraction and critical appraisal

Data extraction of studies included was performed by one reviewer and checked by a

second reviewer. Information on study design, selection criteria, study

population/patient characteristics, and interventions were extracted into a data

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Page 7 of 22

extraction form, followed by individual study treatment effects and associated

uncertainty measures for the outcomes of interest. The methodological quality of the

included studies was assessed with the NICE critical assessment checklist,11 as adapted

from the Centres for Reviews and Dissemination (CRD) checklist for RCTs.12 The risk of

bias in each individual study was evaluated based on the following items: adequate

method of randomisation, adequate allocation concealment, similarity of groups,

blinding, no unexpected imbalances in drop-out, no selective reporting and appropriate

use of the intention-to-treat principle. The results of the critical appraisal of included

studies are presented in Supplementary Material Table S5.

Network meta-analysis feasibility assessment

The feasibility assessment was performed in three steps: (i) the possibility of

constructing a network of interlinked studies, (ii) study design and patient characteristics

that could modify the relative treatment effect were investigated and (iii) data

availability per outcome of interest was assessed. The efficacy outcomes of interest were

ARR at 12 and 24 months, ARR at any reported time point, difference in change from

baseline EDSS score at 12 or 24 months, difference in change from baseline EDSS score

at any time point, and hazard ratio of 3- and 6-month confirmed disability progression.

Disability progression was selected as one of the key outcomes in this study because it

has driven the health economic modelling of RRMS since the first health economic model

developed in 2003 by the School of Health and Related Research (ScHARR).13

To reduce the risk of bias in a NMA, only data from studies with similar study design and

patient populations should be compared. Although some variation in study or patient

characteristics across studies can be expected, an NMA is only valid when no imbalances

exist across comparisons in the study of patient characteristics that can act as effect

modifiers.14 To assess the feasibility of a valid NMA, the network of interlinked RCTs was

analysed for differences in study design, patient characteristics and outcome definitions

that could potentially bias the relative treatment effects.

The similarity of studies in the HA and RES RRMS populations was assessed, by

evaluating the study design, patient population and outcome definitions of studies

identified in the SLR.15

Statistical Analysis

The relative efficacy of the identified interventions for the treatment of HA or RES RRMS

for the selected outcomes was evaluated using a Bayesian NMA. In a Bayesian analysis,

credible intervals (CrI) are used instead of confidence intervals (CI). Credible intervals

assume the true value of the point estimate is within 95% of the range, whereas

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Page 8 of 22

confidence intervals assume that if the analysis was replicated 100 times, 95% of the

confidence intervals would include the true value of the parameter. Trial results were

reported as trial-based summary measures, i.e., 3-month and 6-month confirmed

disability progression at 24 months were reported as hazard ratios and annualized

relapse rates (ARR) at 24 months were reported as risk ratios. In these cases, we

assumed a Normal distribution for the continuous measure of the treatment effect. The

modeling is performed in the log scale. The outputs of the analyses are summary

measures i.e, hazard ratios and risk ratios of the treatment of interest vs the

comparator. A value equal to 1 translates to no difference between the competing

treatments and a value lower than 1 translates to greater efficacy (lower hazard and/or

lower risk of relapse).

Non-informative prior distributions were assumed for both outcomes. In the presence of

non-informative priors, CrIs can be interpreted similarly to confidence intervals using a

frequentist approach. In addition, if the 95% CrIs do not include 1, results can be

considered statistically significant when using non-informative priors. Prior distributions

of the relative treatment-effects were assumed to be normal, with zero mean and a

variance of 10,000, while a uniform distribution with support from 0 to 5 was used as

prior of the between-study standard deviation.

For each of the outcomes, fixed and random effects models were evaluated and the

better fitting model was selected based on the deviance information criterion (DIC)

which adds a penalty term, equal to the number of effective parameters. The fixed-effect

model assumes that there is no variation in the relative treatment effects across studies

for a particular pairwise comparison. The observed differences for a particular

comparison among study results are solely due to chance. The general fixed-effects

model for network meta-analysis can be specified as follows:

where �� is the outcome for treatment b in study j, and �� is the fixed effect of

treatment k relative to treatment b.

The random effects model assumes that the true relative effects are exchangeable

accross studies and can be described as a sample from a Normal/Gaussian distribution

whose mean is the pooled relative effect and SD reflects the heterogeneity. The model

notation of the random effects model is as follows:

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where �� are the trial-specific effect of treatment k relative to treatment b. These trial-

specific effects are drawn from a random-effects distribution with the following

properties: ��~�� , � �.16

Given the small number of studies included in the analyses (one publication per direct

comparison), the fixed-effects model was chosen over the random effects model. The

posterior densities for unknown parameters were estimated using Markov chain Monte

Carlo (MCMC) simulations. The results presented herein were based on 80,000 iterations

on two chains, with a burn-in of 20,000 iterations. Convergence was assessed by visual

inspection of trace plots. The accuracy of the posterior estimates was assessed using the

Monte Carlo error for each parameter (Monte Carlo error <5% of the posterior standard

deviation). All models were implemented using OpenBUGS version 3.2.2 (MRC

Biostatistics Unit, Cambridge, UK) and Rstudio (R version 3.1.2) and were based on the

models defined by Dias et al.16

The Bayesian NMA provided joint posterior distributions of the relative treatment effects

across interventions accompanied with pairwise probabilities of one treatment being

better than another for each of the outcomes. These probabilities were calculated based

on the proportion of MCMC cycles in which a specific treatment estimate was better than

the comparator and can be interpreted as there is x% probability that treatment A is

better than treatment B. The ranking probabilities are summarized by a median and an

associated 95% CrI. Additional ranking outcomes monitored are the probability of being

best (Pbest) and SUCRA. The former is calculated as the proportion of MCMC cycles

which a given treatment ranks first out of all competing interventions. The SUCRA

measure was calculated as Surface Under the Cumulative Ranking Curve (SUCRA);

SUCRA is 1 when a treatment is certain to be the best and 0 when a treatment is certain

to be the worst.

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RESULTS

Search and selection results

The searches identified a total of 5,781 records, of which 1,070 were removed as

duplicates. The PRISMA diagram of the screening process is presented in Figure 1. After

merging with the previous SLR from 2010, 8 records were identified that reported data

either for HA RRMS (N=4) and RES RRMS (N=3) or both separately (n=1). Different

subgroup definitions were used across publications, therefore all publications with

borderline HA or RES RRMS patients were thoroughly evaluated using patient

characteristics and any other details in the publications.

Three full text publications, one conference abstract and the EPAR for dimethyl fumarate

(DMF) were identified in the SLR that presented results for HA RRMS patients.17-21 The

other EPARs did not present subgroup data for HA RRMS. Except for CARE-MS-II, all

studies presented post-hoc subgroup analysis of a RCT or a clinical trial program. It

should be noted that Khatri et al. does not explicitly specify that these patients were

specified to have HA RRMS.17 The CARE-MS-II study included patients with at least two

attacks in the previous 2 years of which at least one in the previous year, at least one

relapse while on interferon beta or glatiramer after at least 6 months of treatment and

an expanded disability status scale (EDSS) scores of 5.0 or less.21 This population was

deemed to be borderline HA RRMS, and it was decided to include this study in the SLR.

In addition, four publications were identified in the SLR for RES RRMS,19 22-25

supplemented by a non-published subgroup analysis in RES RRMS which was provided

by the company.23 Note that one publication presented data for both the HA and RES

RRMS subgroups.19 Two publications were identified presenting post-hoc subgroup

analyses of the AFFIRM study in a HA RRMS publication.24 25 The inclusion criteria for the

subgroup were, however, in line with the RES RRMS definition as presented earlier.

Furthermore, Edan et al. included patients with aggressive relapsing multiple sclerosis,

defined as two or more relapses in the last 12 months or EDSS increases by two or more

points (unconfirmed at three months but assessed outside a relapse) and one or more

gadolinium enhancing lesions on MRI.22 This population definition was deemed similar

enough to RES RRMS to be included in the SLR.

Feasibility of network meta-analysis


The first step in assessing the feasibility of a NMA is to examine the evidence base and

to determine whether a network of studies can be constructed. Treatment arms should

be classified under exclusive categories similar enough to group together in the NMA.

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Separate networks were constructed for HA RRMS and RES RRMS, linking studies to each

other through common comparators.

In the HA RRMS network, fingolimod could be linked to DMF using placebo as the

common comparator (Figure 2). Both studies reported ARR and 3-month confirmed

disability progression at 24 months. CARE-MS-II could not be linked to the network for

lacking a common comparator,21 and TRANSFORMS could not be linked for measuring

outcomes at 12 months instead of 24 months in the other studies.17 As a result, these

studies could not be included in the NMA. Furthermore, Devonshire 2012 reported

subgroup data of the FREEDOMS study,19 which was superseded by the results of

Bergvall et al.,20 whom reported subgroup data for both FREEDOMS and FREEDOMS II.

For RES RRMS, fingolimod was linked to natalizumab through placebo as the common

comparator (Figure 2). Both studies reported ARR, 3-month confirmed disability

progression and 6-month confirmed disability progression at 24 months. The study by

Edan et al. could not be connected to the network because the study lacked a common

comparator and reports results at 3 months instead of 24 months as reported in the

other studies.22 As with the HA RRMS network, the publication by Devonshire reported

subgroup data of the FREEDOMS study only and was superseded by the combined

subgroup data of FREEDOMS and FREEDOMS II as provided by the company.19 23

Study and patient characteristics

The studies included were all post-hoc subgroup analyses of double blind, parallel group,

multicentre, Phase III, RCTs. The subgroup analysis for natalizumab reported on one

RCT (AFFIRM), whereas fingolimod and DMF were supported by pooled analysis of two

studies (FREEDOMS/FREEDOMS II and DEFINE/CONFIRM respectively).26-29 The studies

were all conducted over a 24 month duration and the subgroup analyses were reported

at end of study. The subgroup definitions were similar across studies with regard to

treatment experience, relapses and MRI findings (Table 1). Details on the critical

appraisal of studies are presented in Supplementary Material Table S5. Many items of

the risk of bias assessment were not well reported and therefore the risk of bias of the

included subgroup analyses is unclear. Due to the small number of studies in each

network, it was not possible to create funnel plots to assess publication bias.

Patient population characteristics were not always reported for the HA and RES RRMS

subgroups (Supplementary Material Tables S6). The DMF EPAR did not report any patient

characteristics of the subgroup with high disease activity, and the subgroup analysis of

AFFIRM only reported the number of relapses in the year prior to study entry. Although

no major differences are expected due to the similarity of subgroup definitions, the lack

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of reported patient characteristics made it difficult to assess the distribution of potential

effect modifiers in the network.

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Table 1. Key study characteristics for all included studies in the NMA (only arms of interest)

Study Intervention(s) Study design Study

duration

Subgroup definition

Highly active RRMS

Subgroup analysis

of FREEDOMS and

FREEDOMS II20

Fingolimod 0.5 mg

OD

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCTs

24 months Patients with high disease activity despite previous DMT use as specified in

one of the following subgroups:

Subgroup 1: those with one or more relapses in the previous year and either

one or more gadolinium (Gd) enhancing T1 lesions or at least nine T2 lesions

at baseline;

Subgroup 2: those with the same number or more relapses in the year

before baseline than in the previous year.

EPAR DMF18 Dimethyl fumarate

240 mg BID

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCTs

24 months Patients having at least one relapse in the past year while on therapy with

beta-interferon, and at least 9 T2- hyperintense lesions in cranial MRI or at

least 1 Gd-enhancing lesion or having an unchanged or increased relapse

rate.


Subgroup analysis

of FREEDOMS and

FREEDOMS II23

Fingolimod 0.5 mg

OD

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCTs

24 months Treatment naïve and at least 2 relapses in year-1 and at least 1 Gd+ lesion

at baseline

Subgroup analysis

of AFFIRM24 25

Natalizumab 300 mg

every 4 weeks

Placebo

Post-hoc subgroup

analysis of double-


multicentre RCT

24 months Subgroup of patients that met the criteria for treatment-naïve highly active

relapsing MS (≥2 relapses in the year prior to study entry and ≥1 Gd+ lesion

on T1-weighted MRI at study entry)


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Network Meta-Analysis results

While limited networks could be constructed for both HA and RES RRMS, the lack of

reported patient characteristics made it difficult to assess the risk of bias in the post-hoc

subgroup analyses and to evaluate the distribution of effect modifiers. Nonetheless, the

subgroup definitions were very similar in terms of required number of relapses, MRI

findings and treatment experience. The subgroup definitions were discussed with a

clinical expert, to validate the inclusion of studies in each network. As a result, a NMA

was deemed feasible but should be interpreted with caution due to the low number of

studies and lack of reported baseline characteristics.

Given the geometry of both networks with a low number of studies and no closed loops

(Figure 2) it was not possible to evaluate whether direct and indirect evidence were in

agreement in closed loops. The individual study results are presented in Table 2.

Table 2. Individual study results for all outcomes of interest in the NMA

Annualized relapse

rate at 24 months

(ARR ratio, 95% CI)

3-month confirmed


at 24 months

(HR, 95% CI)

6-month confirmed


at 24 months

(HR, 95% CI)

Highly active RRMS


FREEDOMS and FREEDOMS II20

0.52 (0.40, 0.69) 0.66 (0.45, 0.96) N.A.

EPAR DMF18 0.57 (0.39, 0.84) 1.19 (0.66, 2.15) N.A.



FREEDOMS and FREEDOMS II23

0.43 (0.25, 0.77) 0.76 (0.30, 1.92) 0.67 (0.22, 2.00)


AFFIRM24 25

0.25 (0.16, 0.39) 0.47 (0.24, 0.93) 0.36 (0.17, 0.76)

N.A.: Not applicable, NMA not feasible for this outcome. ARR: annualized relapse rate;

CI: confidence interval; HR: hazard ratio.

Highly Active RRMS

Both active treatments investigated were more efficacious than placebo and

demonstrated lower ARR at 24 months (Figure 3). The results demonstrated no

statistically significant difference in ARR at 24 months between fingolimod 0.5mg OD and

DMF 240mg BID; mean rate ratio 0.91 (95% CrI: 0.57, 1.47). Table 3 presents the

median rank (and 95% CrI), and the probability of being the best treatment and SUCRA

values. Fingolimod 0.5 mg OD has 64.0% probability to be the best treatment, followed

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by DMF 240 mg BID and placebo. The SUCRA values provided identical results regarding

ranking, placing fingolimod 0.5 in first rank (82%) and DMF 240 mg BID in second rank

(67.9%).

Table 3. Estimated ranking of interventions according to different outcomes

Interventions Median rank

(95% CrI)

P

(best) SUCRA (%)

HA RRMS

ARR at 24 months

Fingolimod 0.5 mg OD 1 (1, 2) 64.0% 82.0%

DMF 240 mg BID 2 (1, 2) 36.0% 67.9%

Placebo 3 (3, 3) 0.0% 0.1%


Natalizumab 300 mg 1 (1, 2) 79.2% 89.0%

Fingolimod 0.5 mg OD 2 (1, 3) 20.4% 46.1%

Placebo 3 (2, 3) 0.4% 14.9%

RES RRMS

ARR at 24 months

Natalizumab 300 mg 1 (1, 2) 93.1% 96.5%

Fingolimod 0.5 mg OD 2 (1, 2) 6.9% 53.4%

Placebo 3 (3, 3) 0.0% 0.1%


Natalizumab 300 mg 1 (1, 2) 79.2% 89.0%

Fingolimod 0.5 mg OD 2 (1, 3) 20.4% 46.1%

Placebo 3 (2, 3) 0.4% 14.9%


Natalizumab 300 mg 1 (1, 2) 81.9% 90.8%

Fingolimod 0.5 mg OD 2 (1, 3) 49.4% 47.0%

Placebo 3 (3, 3) 0.1% 12.2%

While fingolimod was able to demonstrate a statistically significant improvement in 3-

month confirmed disability progression at 24-months over placebo, the difference

between DMF and placebo was not statistically significant (Figure 3). The HA subgroups

were unable to demonstrate statistically significant differences in 3-month confirmed

disability progression for the comparison of fingolimod and DMF. The estimated hazard

ratio was found 0.55 (95% CrI: 0.27, 1.12) in favour or fingolimod. Fingolimod 0.5 mg

OD showed 94.0% probability to be the best treatment and ranked first among dimethyl

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fumarate 240mg BID and placebo in the analysis for three month confirmed disability

progression, with a SUCRA of 96.7%.

Rapidly Evolving Severe RRMS

Both active treatments demonstrated a statistically significant improvement in

annualized relapse rate versus placebo at 24-months. No statistically significant

difference was found for the comparison of fingolimod 0.5 mg OD and natalizumab 300

mg regarding ARR at 24 months; mean rate ratio was estimated to be 1.72 (95% CrI:

0.84, 3.53). All pairwise treatment effects can be found in Figure 4. There was a

significant overlap between therapies with respect to the relative ranking and probability

of being the best across treatments for this outcome.

Similar findings were identified for 3-month confirmed disability progression at 24

months, where the comparison between fingolimod and natalizumab was not deemed

statistically significant (Figure 4). Similarly to annualised relapse rates, there is

significant overlap in the CrI when ranking these therapies. SUCRA values were in

accordance with the results of P(best).

The pattern of results was identical for 6-month confirmed disability progression at 24

months showing no statistically significant difference between fingolimod 0.5 mg OD and

natalizumab 300 mg yet wider credible intervals; hazard ratio of 1.86 (95% CrI: 0.49,

7.12). Again, there were significant overlaps in the median rank CrI between therapies;

however, SUCRA values were quite similar to the probabilities of being the best

treatment for this outcome.

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DISCUSSION

Key findings and implications

In the absence of RCTs comparing all interventions of interest, an NMA is an alternative

to obtain relative efficacy estimates. The evidence identified from the SLR and the

feasibility analysis performed revealed the scarcity of available subgroup data in MS

clinical trials, resulting in limited data to be synthesised in an NMA for the subgroups of

interest (HA and RES). Despite the scarcity of data and the lack of information on patient

and study design characteristics, small networks were constructed for each subgroup,

providing analyses for the key outcomes of ARR at 24 months, 3-month confirmed

disability progression at 24 months and 6-month confirmed disability progression at 24

months (only for the RES RRMS subgroup).

The NMA results regarding the HA subgroup demonstrated no statistically significant

difference between fingolimod and DMF on ARR and disability progression; mean rate

ratio of 0.91 (95% CrI: 0.57, 1.47) and hazard ratio of 0.55 (95% CrI: 0.21, 1.12),

respectively.

For the RES subgroup, no statistically significant difference was found for the comparison

of fingolimod with natalizumab for both ARR and disability progression (3-month and 6-

month confirmed); mean rate ratio of 1.72 (95% CrI: 0.84, 3.52) and hazard ratio of

1.62 (95% CrI: 0.51, 5.13) for 3-month confirmed disability progression and 1.86 (95%

CrI: 0.49, 7.12) for 6-month confirmed disability progression, respectively.

Given the limited evidence base, the results of the analyses should be interpreted with

caution. It should also be noted that all included studies were post-hoc subgroup

analyses of large randomised trials, which were not powered to detect a statistically

significant difference between interventions in the HA or RES RRMS subgroups.

Strengths and limitations

The first strength of this analysis was the comprehensive and robust search strategy

which was developed to identify all relevant interventions for the treatment of RRMS.

The evidence base was subsequently tailored to HA and RES subgroups, causing

potential bias to the analyses performed since the baseline characteristics of the included

studies could not be adequately evaluated. Therefore, a thorough assessment of the

similarity of subgroup definitions was performed prior to the full analysis. Not all

publications used the same definition of HA and RES RRMS, therefore all studies with

borderline HA or RES RRMS populations or subgroups were reviewed by a clinical expert.

Studies were only synthesised in the NMA if the subgroup definitions were similar.

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Although it is possible to adjust for potential treatment effect modifiers by performing

meta-regression or sensitivity analysis excluding studies with differences in effect

modifiers, the limited evidence base in the current analysis did not allow for such

analyses. Furthermore, studies were only synthesized if the patient populations were

similar, i.e. using the same definitions for HA and RES RRMS. This limited the impact of

potential imbalances on the results of the NMA. Although the studies were deemed

similar enough to be synthesised in an NMA, residual confounding may still exist in the

aggregated data. Although subgroup data have been considered in the evaluation of

disease-modifying treatments by the NICE Health Technology Assessment groups (for

example alemtuzumab30), these data are not publically available, and could thus not be

utilised in the NMA. However, the results of the NMA for the HA RRMS population were

the same as those reported by the manufacturer in the 2014 Scottish Medicines

Consortium (SMC) submission for fingolimod,31 indicating that new data for these

subgroups has not been published recently.

Conclusion

The lack of data and resulting high level of uncertainty around the NMA estimates of

comparative treatment effectiveness for patients with HA or RES RRMS provides a

challenge to Health Technology Assessment groups appraising the evidence and for the

strength of the recommendations in clinical guidelines. 32 An NMA can offer point

estimates for inclusion in economic models but these estimates will be associated with

high levels of uncertainty which would be further compounded if considered as a basis

for HTA decision making. Until there is a major change in the available data for the

treatments used in these indications, such as additional studies of the DMTs of interest in

HA and RES RRMS, it will be difficult for HTA assessment groups to make reimbursement

decisions on behalf of patients with HA and RES RRMS and the healthcare professionals

who support them.

Figure 1. Study identification flow diagram

Figure 2. Feasible networks for (A) HA RRMS, (B) RES RRMS

Figure 3. NMA results for HA RRMS

Figure 4. NMA results for RES RRMS

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Acknowledgements

The authors thank Julien Gagnon and Amina Udechuku, also employees of Mapi, for

helping with the submission process.

Contributors

The study design and protocol were developed by ZF, JJ, and MB in collaboration with FA

and NA. ZF, JJ, MB, FA, and NA collected the data and conducted the SLR. EH and KP

performed the feasibility study and the network meta-analysis. The manuscript was

written by EH, KP, JJ, MB, and NA. All authors have been involved in reviewing the study

outcomes and have approved the final version of the manuscript.

Funding

The study and manuscript were funded by Novartis.

Competing interests

FA and NA are employees of Novartis. EH, KP, ZF, JJ and MB are employees of Mapi, and

served as paid consultants to Novartis to conduct the systematic literature review and

preparation of this manuscript. All authors have been involved in the review of the

systematic literature review, the model results, and the manuscript.

Data sharing statement

No additional data are available.

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in the past year and at least 1 Gd+ T1 lesion at baseline. Data on file.



Journal of neurology 2009;256(3):405-15.



2013;260(5):1388-95.

26. Calabresi PA, Radue EW, Goodin D, et al. Safety and efficacy of fingolimod in

patients with relapsing-remitting multiple sclerosis (FREEDOMS II): a double-

blind, randomised, placebo-controlled, phase 3 trial. The Lancet Neurology

2014;13(6):545-56.

27. Kappos L, Radue EW, O'Connor P, et al. A placebo-controlled trial of oral fingolimod

in relapsing multiple sclerosis. The New England journal of medicine

2010;362(5):387-401.

28. Gold R, Kappos L, Arnold DL, et al. Placebo-controlled phase 3 study of oral BG-12

for relapsing multiple sclerosis. The New England journal of medicine

2012;367(12):1098-107.

29. Fox RJ, Miller DH, Phillips JT, et al. Placebo-controlled phase 3 study of oral BG-12 or

glatiramer in multiple sclerosis. The New England journal of medicine

2012;367(12):1087-97.

30. Excellence NIfHaC. Alemtuzumab for treating relapsing-remitting multiple sclerosis:

Technology apprisal guidance 2014.

31. Scottish Medicines Consortium. fingolimod, 0.5mg, hard capsules (Gilenya) SMC No

(992/14). 2014.

32. Excellence NIfHaC. Multiple sclerosis in adults: management: Clinical guideline

[CG186], 2014.

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Page 22 of 22

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Figure 1. Study identification flow diagram

191x130mm (300 x 300 DPI)

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Figure 2. Feasible networks for (A) HA RRMS, (B) RES RRMS

92x44mm (300 x 300 DPI)

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Figure 3. NMA results for HA RRMS

94x35mm (300 x 300 DPI)

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Figure 4. NMA results for RES RRMS

91x31mm (300 x 300 DPI)

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Table S1: Search strategy for the systematic literature review - Medline search


# Search terms Hits

1 Multiple Sclerosis/ OR Multiple Sclerosis, Relapsing-Remitting/ OR Myelitis, Transverse/ OR Demyelinating Diseases/ OR Encephalomyelitis, Acute Disseminated/

55747

2 ("multiple sclerosis" OR "transverse myelitis" OR "optic neuritis" OR "devic" OR "adem" OR "neuromyelitis optica").tw.

56922

3 1 OR 2 72672

4 (Fingolimod OR Gilenya OR FTY720 OR FTY-720 OR FTY 720 OR fingolimod hydrochloride OR 2-amino-2-2-4-octylphenylethyl-1,3-propanediol

hydrochloride).tw.

1601

5 exp Interferon-beta/ or (Interferon beta* or IFN-beta or rebif* or avonex or betaseron or Fibroblast IFN or Interferon-beta* or betaferon or IFN beta precursor or IFN beta* or IFNbeta* or Extavia).tw.

12828


polymer1 OR Co polymer 1 OR COP1 OR COP 1 OR TV 5010 OR TV-5010 OR TV5010).tw.

1695

7 (Cladribine OR Leustat OR Leustatin OR 2CDA OR Mylinax OR Movectro OR 2 chloro 2 deoxyadenosine OR 2chloro2deoxyadenosine OR 2chloro 2deoxyadenosine OR 2chloro2deoxybetaadenosine OR 2 chloro 2 deoxy

beta adenosine OR 2 chloro 2 deoxybetaadenosine OR 2chloro 2deoxybetaadenosine OR 2 Chlorodeoxyadenosine OR 2Chlorodeoxyadenosine OR Chloroadenosine).tw.

2660

8 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4 integrin OR Anti-alpha4integrin).tw.

1333


10 (Teriflunomide OR HMR1726 OR Flucyamide OR Aubagio OR A77 1726 OR SU 20 OR A 771726 OR HMR 1726 OR HMR-1726 OR UNII-1C058IKG3B OR A771726).tw.

331




1572

12 (Alemtuzumab OR MabCambath OR Campath OR Lemtrada OR UNII-3A189DH42V OR 126775-97-1 OR 216503-57-0 OR 478159-77-2 OR

727728-72-5).tw.

2232

13 OR/4-12 24039

14 (clinical trial or Controlled Clinical Trial).pt. or exp Randomized Controlled Trials as Topic/ or (Randomised Clinical Trial or Randomised Clinical Trials or Randomized Clinical Trial or Randomized Clinical Trials or Randomised

Controlled Trial or Randomized Controlled Trials or Randomized Controlled Trial or Randomized Controlled Trials or Randomised Trial or Randomised Trials or Randomized Trial or Randomized Trials or Random Allocation or Double Blind Method or Single Blind Method or Placebo* or Allocated Random* or Open-label Trial* or Open-label Stud* or Non-blinded Stud*).tw. or exp Cohort Publications/ or (Cohort Analyses or Cohort Analysis or Longitudinal).tw. or exp Follow-Up Publications/ or evaluation

stud*.tw. or exp Prospective Publications/ or Observational.tw.

2201588

15 3 and 13 and 14 1975


17 15 not 16 1968


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Table S2: Search strategy for the systematic literature review - Embase search


# Search terms Hits

1 multiple sclerosis/ OR myelitis/ OR demyelinating disease/ OR postvaccinal encephalitis/

80634

2 ("multiple sclerosis" OR "transverse myelitis" OR "optic neuritis" OR "devic" OR "adem" OR "neuromyelitis optica").tw.

66562

3 1 OR 2 88993

4 (Fingolimod OR Gilenya OR FTY720 OR FTY-720 OR FTY 720 OR fingolimod hydrochloride OR 2-amino-2-2-4-octylphenylethyl-1,3-propanediol hydrochloride).tw.

3673

5 exp Interferon-beta/ or (Interferon beta* or IFN-beta or rebif* or avonex or betaseron or Fibroblast IFN or Interferon-beta* or betaferon or IFN beta precursor or IFN beta* or IFNbeta* or Extavia).tw.

23572

6 (Glatiramer acetate OR Copaxone OR Copolymer1 OR copolymer 1 OR Co polymer1 OR Co polymer 1 OR COP1 OR COP 1 OR TV 5010 OR TV-5010 OR TV5010).tw.

3557

7 (Cladribine OR Leustat OR Leustatin OR 2CDA OR Mylinax OR Movectro OR 2 chloro 2 deoxyadenosine OR 2chloro2deoxyadenosine OR 2chloro 2deoxyadenosine OR 2chloro2deoxybetaadenosine OR 2 chloro 2 deoxy beta adenosine OR 2 chloro 2 deoxybetaadenosine OR 2chloro 2deoxybetaadenosine OR 2 Chlorodeoxyadenosine OR


2945

8 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4 integrin OR Anti-alpha4integrin).tw.

3680


10 (Teriflunomide OR HMR1726 OR Flucyamide OR Aubagio OR A77 1726 OR SU 20 OR A 771726 OR HMR 1726 OR HMR-1726 OR UNII-1C058IKG3B OR A771726).tw.

789

11 (Dimethyl Fumarate OR Dimethylfumarate OR 624-49-7 OR Fumaric Acid OR Dimethyl Ester OR Fumaderm OR Methyl Fumarate OR 2-Butenedioic


2254

12 (Alemtuzumab or MabCambath or Campath or Lemtrada or UNII-3A189DH42V or 126775-97-1 or 216503-57-0 or 478159-77-2 or 727728-72-5).tw.

10699

13 OR/4-12 47452

14 (Clinical trial OR Controlled Clinical Trial).pt. OR exp Randomized Controlled Trials as Topic/ OR (Randomised Clinical Trial OR Randomised Clinical Trials OR Randomized Clinical Trial OR Randomized Clinical Trials OR Randomised Controlled Trial OR Randomized Controlled Trials OR

Randomized Controlled Trial OR Randomized Controlled Trials OR Randomised Trial OR Randomised Trials OR Randomized Trial OR Randomized Trials OR Random Allocation OR Double Blind Method OR Single Blind Method OR Placebo* OR Allocated Random* OR Open-label Trial* OR Open-label Stud* OR Non-blinded Stud*).tw. OR Exp Cohort Publications/ OR (Cohort Analyses OR Cohort Analysis OR Longitudinal).tw. OR Exp Follow-Up Publications/ OR (evaluation stud*).tw OR exp

Prospective Publications/ OR Observational.tw.

1668075

15 3 AND 13 AND 14 3672


17 15 not 16 3672


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Table S3: Search strategy for the systematic literature review - Cochrane

Search String (November 14, 2014)

# Search Terms Hits

1 MeSH DESCRIPTOR [Multiple Sclerosis] explode all trees 1861

2 MeSH descriptor: [Myelitis, Transverse] explode all trees 11

3 MeSH descriptor: [Demyelinating Diseases] explode all trees 2077

4 MeSH descriptor: [Encephalomyelitis, Acute Disseminated] explode all trees

3

5 MeSH descriptor: [Multiple Sclerosis, Relapsing-Remitting] explode all trees

417

6 ("multiple sclerosis" or "transverse myelitis" or "optic neuritis" or "devic" or "adem" or "neuromyelitis optica")

4214

7 #1 or #2 or #3 or #4 or #5 or #6 4418

8 (Fingolimod OR Gilenya OR FTY720 OR FTY-720 OR FTY 720 OR

fingolimod hydrochloride OR 2-amino-2-(2-(4-octylphenyl)ethyl)-1,3-

propanediol hydrochloride)

158

9 MeSH descriptor: [Interferon-beta] explode all trees 520

10 (Interferon beta* or IFN-beta or rebif* or avonex or betaseron or Fibroblast IFN or Interferon-beta* or betaferon or IFN beta precursor or IFN beta* or IFNbeta* or Extavia)

1526

11 (Glatiramer acetate OR Copaxone OR Copolymer1 OR copolymer 1 OR Co polymer1 OR Co polymer 1 OR COP1 OR COP 1 OR TV 5010 OR TV-5010 OR TV5010)

805

12 (Cladribine OR Leustat OR Leustatin OR 2CDA OR Mylinax OR Movectro

OR 2 chloro 2 deoxyadenosine OR 2chloro2deoxyadenosine OR 2chloro 2deoxyadenosine OR 2chloro2deoxybetaadenosine OR 2 chloro 2 deoxy beta adenosine OR 2 chloro 2 deoxybetaadenosine OR 2chloro 2deoxybetaadenosine OR 2 Chlorodeoxyadenosine OR 2Chlorodeoxyadenosine OR Chloroadenosine)

192


integrin OR Anti-alpha4integrin)

156

14 (Teriflunomide OR HMR1726 OR Flucyamide OR Aubagio OR A77 1726 OR SU 20 OR A 771726 OR HMR 1726 OR HMR-1726 OR UNII-1C058IKG3B OR A771726)

3149

15 (Dimethyl Fumarate OR Dimethylfumarate OR 624-49-7 OR Fumaric Acid OR Dimethyl Ester OR Fumaderm OR Methyl Fumarate OR 2-Butenedioic Acid OR Dimethyl Ester OR Tecfidera OR FAG 201 OR FAG201 OR BG12 OR BG-12 OR BG00012 OR BG-00012)

143

16 Alemtuzumab OR MabCambath OR Campath OR Lemtrada OR UNII-3A189DH42V OR 126775-97-1 OR 216503-57-0 OR 478159-77-2 OR

727728-72-5

285

17 #8 or #9 or #10 or #11 or #12 or #13 or #14 or #15 or #16 6018

18 #7 and #17 [Publication Year from 2010, in Trials and Reviews] 353

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Table S4: Patients, interventions, comparisons, outcomes and study design

(PICOS) criteria for inclusion in the SLR

Criteria Inclusion

Patient

population

Abstract

screening

Adults with all RRMS including:

Active RRMS

HA RRMS*

RES RRMS*

Full text

screening Adults with HA RRMS or RES RRMS*

Intervention

Abstract

screening

Fingolimod

Beta interferon

Glatiramer acetate

Natalizumab

Teriflunomide

Dimethyl fumarate

Alemtuzumab

Full text

screening

Licenced treatments in HA RRMS:

Fingolimod

Beta interferon

Glatiramer acetate

Teriflunomide

Dimethyl fumarate

Alemtuzumab

Licenced treatments in RES RRMS:

Fingolimod

Natalizumab

Comparison (any

dosage)

Abstract

and full

text

screening

Any of the interventions above or best supportive

care

Outcomes

Abstract

and full

text

screening

Functional Outcomes

Annualized relapse rate (ARR)

ARR ratio

Hazard ratio (HR) for time to relapse

HR for disability progression (at 3 and 6

months or otherwise)

Proportion of patients with no relapses

Mean change from baseline in EDSS score

Proportion of patients disease activity free

Proportion of patients with no change in EDSS

MRI Outcomes

Mean number of new or enlarged T2 hyper

intense lesions

Proportion of patients with no T2 lesions

Mean MS Functional composite scale z-score

Study design

Abstract

and full

text

screening

Randomised controlled trials

* Highly active RRMS was defined as having an unchanged or increased relapse rate or on-going severe relapses compared with the previous year despite treatment with a DMT, RES RRMS was defined as two or more disabling relapses in the past year, and one or more gadolinium-enhancing lesions on MRI or increase in T2 lesion load compared with previous MRI. ARR: Annualised relapse rate; HR: Hazard ratio; EDSS: Expanded disability status scale; MRI: Magnetic Resonance Imaging.

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Table S5: Risk of bias assessment of included studies

Study Was the

method used

to generate

random

allocations

adequate?

Was the

allocation

adequately

concealed?

Were the

groups

similar at

the outset of

the study

terms of

prognostic

factors, for

example,

severity?

Were the

care

providers,

participants

and outcome

assessors

blind to

treatment

allocation?

Were there

any

unexpected

imbalances

in drop-outs

between

groups?

Is there any

evidence to

suggest that

the authors

measured

more

outcomes

than they

reported?

Did the analysis

include an

intention-to-treat

analysis? If so,

what was the

appropriate

method used to

account for

missing data?

Highly active RRMS

Subgroup analysis

of FREEDOMS and

FREEDOMS II[1]


EPAR DMF[2] Unclear Unclear Unclear Yes Unclear Unclear Unclear


Subgroup analysis

of FREEDOMS and

FREEDOMS II[3]

Unclear Unclear Unclear Yes Unclear Unclear Unclear

Subgroup analysis

of AFFIRM[4,5]


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Table S6. Key patient characteristics at baseline and study results for all included studies in the NMA (only arms of

interest)

Author and

year

Treatment Number of

patients

% female Mean age

(years)

Mean

duration of

MS since

diagnosis

(years)

Mean

number of

relapses in

past year

Mean

baseline

EDSS score

Mean

number of

GAD

enhancing

lesions

Highly active RRMS

Subgroup

analysis of

FREEDOMS and

FREEDOMS II[1]

Fingolimod 0.5mg OD 249 76.3% 39.3 6.3 1.5 2.5 1.9

Placebo 257 74.7% 39.2 6.2 1.6 2.7 1.3

EPAR DMF[2] DMF 240 mg BID NR NR NR NR NR NR NR

Placebo NR NR NR NR NR NR NR

RES RRMS

Subgroup

analysis of

FREEDOMS and

FREEDOMS II[3]

Fingolimod 0.5mg OD 59 72.9% 32.4 1.8 2.4 2.3 NR

Placebo 47 66.0% 32.8 3.7 2.3 2.0 NR

Subgroup

analysis of

AFFIRM[4,5]

Natalizumab 300 mg

every 4 weeks

148 NR NR NR 2.45 NR NR

Placebo 61 NR NR NR 2.28 NR NR


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References




2. European Medicines Agency. European public assessment reports Tecfidera (Common

Name: dimethyl fumarate). 2013.



3. Novartis. Subgroup analysis of treatment naive patients who had at least 2 relapses in

the past year and at least 1 Gd+ T1 lesion at baseline. Data on file






2013;260(5):1388-95

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http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Public_assessment_report/human/002601/WC500162070.pdf

http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Public_assessment_report/human/002601/WC500162070.pdf



PRISMA NMA Checklist of Items to Include When Reporting A Systematic Review

Involving a Network Meta-analysis

Section/Topic Item

#

Checklist Item Reported on Page

#

TITLE

Title 1 Identify the report as a systematic review

incorporating a network meta-analysis (or

related form of meta-analysis).

Page 1

ABSTRACT

Structured

summary

2 Provide a structured summary including,

as applicable:

Background: main objectives

Methods: data sources; study eligibility

criteria, participants, and interventions;

study appraisal; and synthesis methods,

such as network meta-analysis.

Results: number of studies and

participants identified; summary estimates

with corresponding confidence/credible

intervals; treatment rankings may also be

discussed. Authors may choose to

summarize pairwise comparisons against a

chosen treatment included in their

analyses for brevity.

Discussion/Conclusions: limitations;

conclusions and implications of findings.

Other: primary source of funding;

systematic review registration number

with registry name.

Page 2

INTRODUCTION

Rationale 3 Describe the rationale for the review in the Pages 4/5 – end of

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context of what is already known,

including mention of why a network meta-

analysis has been conducted.

introduction

Objectives 4 Provide an explicit statement of questions

being addressed, with reference to

participants, interventions, comparisons,

outcomes, and study design (PICOS).

Pages 4/5 – end of

introduction

METHODS

Protocol and

registration

5 Indicate whether a review protocol exists

and if and where it can be accessed (e.g.,

Web address); and, if available, provide

registration information, including

registration number.

Page 6 – Data

sources

Eligibility criteria 6 Specify study characteristics (e.g., PICOS,

length of follow-up) and report

characteristics (e.g., years considered,

language, publication status) used as

criteria for eligibility, giving rationale.

Clearly describe eligible treatments

included in the treatment network, and

note whether any have been clustered or

merged into the same node (with

justification).

Supplementary


Information

sources

7 Describe all information sources (e.g.,

databases with dates of coverage, contact

with study authors to identify additional

studies) in the search and date last

searched.

Page 6 – Data

sources

Search 8 Present full electronic search strategy for

at least one database, including any limits

used, such that it could be repeated.

Supplementary

Material – Table

S1-S3

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Study selection 9 State the process for selecting studies

(i.e., screening, eligibility, included in

systematic review, and, if applicable,

included in the meta-analysis).

Page 6 – Selection

of studies

Data collection

process

10 Describe method of data extraction from

reports (e.g., piloted forms,

independently, in duplicate) and any

processes for obtaining and confirming

data from investigators.

Pages 6/7 – Data

extraction and

quality assessment

Data items 11 List and define all variables for which data

were sought (e.g., PICOS, funding

sources) and any assumptions and

simplifications made.

Page 7 –

Assessment of

network meta-


Geometry of

the network

S1 Describe methods used to explore the

geometry of the treatment network under

study and potential biases related to it.

This should include how the evidence base

has been graphically summarised for

presentation, and what characteristics

were compiled and used to describe the

evidence base to readers.

Page 7 –

Assessment of

network meta-


Risk of bias

within individual

studies

12 Describe methods used for assessing risk

of bias of individual studies (including

specification of whether this was done at

the study or outcome level), and how this

information is to be used in any data

synthesis.

Page 7 – Data

extraction and

quality assessment

Summary

measures

13 State the principal summary measures

(e.g., risk ratio, difference in means). Also

describe the use of additional summary

measures assessed, such as treatment

rankings and surface under the cumulative

ranking curve (SUCRA) values, as well as

Pages 7/8 –

Assessment of

network meta-

analysis

feasibility/Statistical

analysis

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modified approaches used to present

summary findings from meta-analyses.

Planned methods

of analysis

14 Describe the methods of handling data

and combining results of studies for each

network meta-analysis. This should

include, but not be limited to:

• Handling of multi-arm trials;

• Selection of variance structure;

• Selection of prior distributions in

Bayesian analyses; and

• Assessment of model fit.

Pages 7/8 –

Statistical analysis

Assessment of

Inconsistency

S2 Describe the statistical methods used to

evaluate the agreement of direct and

indirect evidence in the treatment

network(s) studied. Describe efforts taken

to address its presence when found.

Not applicable, no

closed loops

Risk of bias

across studies

15 Specify any assessment of risk of bias that

may affect the cumulative evidence (e.g.,

publication bias, selective reporting within

studies).

Pages 9/10 –

Feasibility of

network meta-

analysis

Additional

analyses

16 Describe methods of additional analyses if

done, indicating which were pre-specified.

This may include, but not be limited to,

the following:

• Sensitivity or subgroup analyses;

• Meta-regression analyses;

• Alternative formulations of the

treatment network; and

• Use of alternative prior

distributions for Bayesian analyses

(if applicable).

Not applicable, not

enough studies, see

pages 16/17 –

Discussion

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RESULTS†

Study selection 17 Give numbers of studies screened,

assessed for eligibility, and included in the

review, with reasons for exclusions at

each stage, ideally with a flow diagram.

Figure 1

Page 9 – Search

and selection

results

Presentation of

network

structure

S3 Provide a network graph of the included

studies to enable visualisation of the

geometry of the treatment network.

Figure 2

Summary of

network

geometry

S4 Provide a brief overview of characteristics

of the treatment network. This may

include commentary on the abundance of

trials and randomised patients for the

different interventions and pairwise

comparisons in the network, gaps of

evidence in the treatment network, and

potential biases reflected by the network

structure.

Pages 9-11 –


Study

characteristics

18 For each study, present characteristics for

which data were extracted (e.g., study

size, PICOS, follow-up period) and provide

the citations.

Supplementary


Risk of bias

within studies

19 Present data on risk of bias of each study

and, if available, any outcome level

assessment.

Supplementary


Results of

individual studies

20 For all outcomes considered (benefits or

harms), present, for each study: 1) simple

summary data for each intervention

group, and 2) effect estimates and

confidence intervals. Modified approaches

may be needed to deal with information

from larger networks.

Table 2

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Synthesis of

results

21 Present results of each meta-analysis

done, including confidence/credible

intervals. In larger networks, authors may

focus on comparisons versus a particular

comparator (e.g. placebo or standard

care), with full findings presented in an

appendix. League tables and forest plots

may be considered to summarisze

pairwise comparisons. If additional

summary measures were explored (such

as treatment rankings), these should also

be presented.

Results presented

per subgroup and

per outcome of

interest

Exploration for

inconsistency

S5 Describe results from investigations of

inconsistency. This may include such

information as measures of model fit to

compare consistency and inconsistency

models, P values from statistical tests, or

summary of inconsistency estimates from

different parts of the treatment network.

Not applicable, no

closed loops

Risk of bias

across studies

22 Present results of any assessment of risk

of bias across studies for the evidence

base being studied.

Page 13 – Network

Meta-Analysis

results

Results of

additional

analyses

23 Give results of additional analyses, if done

(e.g., sensitivity or subgroup analyses,

meta-regression analyses, alternative

network geometries studied, alternative

choice of prior distributions for Bayesian

analyses, and so forth).

Not applicable, not

enough studies, see

page 16 –

Discussion

DISCUSSION

Summary of

evidence

24 Summarise the main findings, including

the strength of evidence for each main

outcome; consider their relevance to key

groups (e.g., healthcare providers, users,

Pages 16/17 – Key

findings and

implications

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and policy-makers).

Limitations 25 Discuss limitations at study and outcome

level (e.g., risk of bias), and at review

level (e.g., incomplete retrieval of

identified research, reporting bias).

Comment on the validity of the

assumptions, such as transitivity and

consistency. Comment on any concerns

regarding network geometry (e.g.,

avoidance of certain comparisons).

Pages 16/17 –

Strengths and

limitations

Conclusions 26 Provide a general interpretation of the

results in the context of other evidence,

and implications for future research.

Page 17 -

Conclusion

FUNDING

Funding 27 Describe sources of funding for the

systematic review and other support (e.g.,

supply of data); role of funders for the

systematic review. This should also

include information regarding whether

funding has been received from

manufacturers of treatments in the

network and/or whether some of the

authors are content experts with

professional conflicts of interest that could

affect use of treatments in the network.

Page 18 -

Disclosure

PICOS = population, intervention, comparators, outcomes, study design.

* Text in italics indicates wording specific to reporting of network meta-analyses that has

been added to guidance from the PRISMA statement.

† Authors may wish to plan for use of appendices to present all relevant information in full

detail for items in this section.

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