bmj open...existing published studies to draw meaningful comparisons between the efficacy of dmts in...
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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
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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-
blind, parallel group,
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-
blind, parallel group,
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-
blind, parallel group,
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
disability progression
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.
Rapidly evolving severe RRMS
Subgroup analysis of
FREEDOMS and FREEDOMS II[23]
0.43 (0.25, 0.77) 0.76 (0.30, 1.92) 0.67 (0.22, 2.00)
Subgroup analysis of
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%
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%
6-month disability progression at 24 months
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
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19. Devonshire V, Havrdova E, Radue EW, et al. Relapse and disability outcomes in
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20. Bergvall NS SN, Chin P, Tomic D, Von Rosenstiel P, and Kappos L. Efficacy Of
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25. Kappos L, O'Connor PW, Polman CH, et al. Clinical effects of natalizumab on multiple
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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
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28. Gold R, Kappos L, Arnold DL, et al. Placebo-controlled phase 3 study of oral BG-12
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29. Fox RJ, Miller DH, Phillips JT, et al. Placebo-controlled phase 3 study of oral BG-12 or
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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
string (November 14, 2014)
# 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
2Chlorodeoxyadenosine OR Chloroadenosine).tw.
2945
8 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4
integrin OR Anti-alpha4integrin).tw.
3680
9 (Best Supportive Care or Best Available Care).tw. 2278
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
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
16 animals/ not humans/ 586556
17 15 not 16 3672
18 limit 17 to yr="2010 -Current" 2460
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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
13 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4
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
Rapidly evolving severe RRMS
Subgroup analysis
of FREEDOMS and
FREEDOMS II[3]
Unclear Unclear Unclear Yes Unclear Unclear Unclear
Subgroup analysis
of AFFIRM[4,5]
Yes Yes Yes Yes Unclear Unclear Unclear
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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
BID: twice daily; DMF: Dimethyl fumarate; OD: once daily.
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References
1. Bergvall NS SN, Chin P, Tomic D, Von Rosenstiel P, and Kappos L. Efficacy Of
Fingolimod In Pre-Treated Patients With Disease Activity: Pooled Analyses Of
FREEDOMS and FREEDOMS II Neurology 2014;82(10):Suppl P3.174
2. European Medicines Agency. European public assessment reports Tecfidera (Common
Name: dimethyl fumarate). 2013.
http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-
_Public_assessment_report/human/002601/WC500162070.pdf.
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
4. Hutchinson M, Kappos L, Calabresi PA, et al. The efficacy of natalizumab in patients
with relapsing multiple sclerosis: subgroup analyses of AFFIRM and SENTINEL.
Journal of neurology 2009;256(3):405-15
5. Kappos L, O'Connor PW, Polman CH, et al. Clinical effects of natalizumab on multiple
sclerosis appear early in treatment course. Journal of neurology
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-
analysis feasibility
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 –
Network availability
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
Material – Table S6
Risk of bias
within studies
19 Present data on risk of bias of each study
and, if available, any outcome level
assessment.
Supplementary
Material – Table S5
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
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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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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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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 meta-analysis
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(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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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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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
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
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-
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 DMF18 Dimethyl fumarate
240 mg BID
Placebo
Post-hoc subgroup
analysis of double-
blind, parallel group,
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 II23
Fingolimod 0.5 mg
OD
Placebo
Post-hoc subgroup
analysis of double-
blind, parallel group,
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-
blind, parallel group,
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
disability progression
at 24 months
(HR, 95% CI)
Highly active RRMS
Subgroup analysis of
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.
Rapidly evolving severe RRMS
Subgroup analysis of
FREEDOMS and FREEDOMS II23
0.43 (0.25, 0.77) 0.76 (0.30, 1.92) 0.67 (0.22, 2.00)
Subgroup analysis of
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%
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%
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%
6-month disability progression at 24 months
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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30. Excellence NIfHaC. Alemtuzumab for treating relapsing-remitting multiple sclerosis:
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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
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
string (November 14, 2014)
# 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
2Chlorodeoxyadenosine OR Chloroadenosine).tw.
2945
8 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4 integrin OR Anti-alpha4integrin).tw.
3680
9 (Best Supportive Care or Best Available Care).tw. 2278
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
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
16 animals/ not humans/ 586556
17 15 not 16 3672
18 limit 17 to yr="2010 -Current" 2460
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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
13 (Natalizumab OR Tysabri OR Antegren OR Anti-VLA4 OR Anti-alpha4
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
Rapidly evolving severe RRMS
Subgroup analysis
of FREEDOMS and
FREEDOMS II[3]
Unclear Unclear Unclear Yes Unclear Unclear Unclear
Subgroup analysis
of AFFIRM[4,5]
Yes Yes Yes Yes Unclear Unclear Unclear
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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
BID: twice daily; DMF: Dimethyl fumarate; OD: once daily.
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References
1. Bergvall NS SN, Chin P, Tomic D, Von Rosenstiel P, and Kappos L. Efficacy Of
Fingolimod In Pre-Treated Patients With Disease Activity: Pooled Analyses Of
FREEDOMS and FREEDOMS II Neurology 2014;82(10):Suppl P3.174
2. European Medicines Agency. European public assessment reports Tecfidera (Common
Name: dimethyl fumarate). 2013.
http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-
_Public_assessment_report/human/002601/WC500162070.pdf.
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
4. Hutchinson M, Kappos L, Calabresi PA, et al. The efficacy of natalizumab in patients
with relapsing multiple sclerosis: subgroup analyses of AFFIRM and SENTINEL.
Journal of neurology 2009;256(3):405-15
5. Kappos L, O'Connor PW, Polman CH, et al. Clinical effects of natalizumab on multiple
sclerosis appear early in treatment course. Journal of neurology
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-
analysis feasibility
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 –
Network availability
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
Material – Table S6
Risk of bias
within studies
19 Present data on risk of bias of each study
and, if available, any outcome level
assessment.
Supplementary
Material – Table S5
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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