ClinicSpeak: comparing the efficacy of the oral drugs

Can we use modeling to do virtual head-2-head studies; the relative efficacy of the orals. #MSBlog #MSResearch #ClinicSpeak

“It is becoming increasingly important to demonstrate efficacy, or
relative efficacy, of one DMT over another so that the cost-effectiveness of
the agents can be assessed and the correct level of reimbursement set. Some European
countries have made this a priority. One particular country has asked for data
on the relative efficacy of fingolimod against the other oral drugs on the
market. Short of doing head-2-head studies the next best thing is to model the
drugs against each other using published data. How do you do this? The most
common method is simply to compare the phase 3 trial outcome data. This is fraught
with problems because of the different type of MSers recruited into these
studies and the different time epochs these studies were performed in. In
addition, the relapse rates, or events, in the comparator or placebo arms differ,
making comparison between relative efficacy rates difficult. The best way to
tackle this is statistically is using modeling. I was somewhat surprised that
there are well-developed methods in statistics for doing this. Why hadn’t the
field of MS embraced these methods earlier? My interest in all things MS got me
invited onto a panel to try modeling the outcome and made me realise why the
10,000 hour rule is so important. The statisticians who worked on this project are simply
amazing and I want to take this opportunity to thank Niklas Bergvall, Richard Nixon, Nikolaos Sfikas and Gary Cutter for
opening my eyes to a new world of statistics that the non-expert like me can
only dream of being able to do. Statisticians are seriously under-appreciated; not anymore in my books. Although the methods are very complicated and it took me a
long time to grasp, they are a major improvement on the
comparative methods we have been using to date to compare trial outcomes.”

“I have tried to explain the principles of the modeling method using the embedded slideshow. Most of the
problems with across trial comparisons relate to differences in the study
population. What we did is simply take all the fingolimod trial data and extract from
subpopulations of MSers that match the baseline characteristics of the MSers
studied in the Teriflunomide and DMF studies. When asked how these matched sub-populations
from the fingolimod trials did in achieving NEDA (no evident disease activity) and compared them to the published data from the teriflunomide and DMF trials. To do this analysis you need all the raw trial data from the
fingolimod trials to interrogate and you need the published data from the
teriflunomide and DMF studies. You then define characteristics in the study
population that may affect the outcome; these are referred to in stats speak as
covariates. For the analyses we used 8 covariates: 
  1. Age 
  2. Gender
  3. Previous DMT use 
  4. Duration of MS 
  5. Number of relapses in the past year 
  6. EDSS score at baseline 
  7. Number of Gd-enhancing T1 lesions at baseline
  8. Cube root of the total volume of T2 lesions
The modeling method uses a conservative approach in that it penalises our
assumptions to keep things as simple as possible; it does this by trimming the confidence
intervals of the relative risks attributed to each covariate. By doing this you
limit the models complexity and only keep the covariates that affect outcome. For
the non-statisticians reading this post the so called penalisation factor is
called the Akaike information criterion or AIC. We then assessed the model for
its goodness-of-fit using another set of statistical rules before accepting it.
The message I want to get across is that this assessment is no trivial task and
the results are about as close as you can get to a formal head-2-head study
from the data we had in hand. In other words an in silico head-2-head study.”

“Why NEDA? It is becoming increasingly clear that NEDA, or a form of
minimal evidence of disease activity (MEDA), is becoming the treatment target
among the vast majority of MSologists, with some well-publicised exceptions
(e.g. the United Kingdom). Therefore it made sense to us this outcome as the results may help guide clinical practice. Interestingly, I second guessed these
results based on the primary outcome data of the clinical trials, but it is nice to see that the model
predicted my assumptions.”

“There are limitations to our modeling approach that are based on our assumptions.
For example, we assumed that the outcomes of the trials were influenced by the set
of 8 covariates above; it is likely, that the results could be affected by additional
variables not included in the models, such as the environment at the time these
studies were conducted and/or the neurological practice in countries participating in the studies.
Unfortunately, we can only adjust for known variables and could not account for
subtle unmeasured selection criteria as sources of influence or bias. The other issue is that the way NEDA is reported in these studies is using the baseline or month zero scan. We now rebaseline MSers on DMTs so this analysis will need to be redone when we can access year 2 data only; i.e. NEDA rates after rebaselining at month 12. The current methods put teriflunomide at a disadvantage as it probably takes the longest to start working and it had the most active study subjects based on the event rate in the placebo group. Interestingly, teriflunomide is the only oral DMT to hit significant disability outcomes in both studies, which is why it is level pegging with DMF in our analysis relative to fingolimod. It would be interesting for Genzyme-Sanofi and Biogen-Idec to repeat the same exercise as us using their own data sets; i.e. to triangulate the results. Wouldn’t it be interesting if their results were the same or even differed? I would like to challenge them to do the same.” 

“Will these results affect clinical practice? They are unlikely to in the
UK or Europe, where fingolimod has a 2nd-line license. In the UK you can only use fingolimod after you have failed a 1st-line injectable therapy. I suspect in countries
where fingolimod has a 1st-line license this may affect MSer and
MSologist choice.”

“As far as MSer-choice goes efficacy is only one factors in
the decision-making process. I always tell patients that it is ‘horses for
courses’; gone are the days when we simply prescribe a drug and leave you on it
for years. We now actively monitor response, or non-response, and if you have
breakthrough disease we change your therapy. Therefore, I think much bigger
differentiators for individuals amongst the oral therapies will be pregnancy,
tolerability, side-effects, adherence and safety issues. As I say this I am also acutely
aware that fingolimod will be the first small molecule oral medication that
comes off patent, which should pave the way for a cheap generic DMT for MSers.
This in my opinion will be the most disruptive factor to face the MS DMT market. Any
guesses on whether or not the EMA will change fingolimod’s license? Let’s hope
so for the sake of MSers. 
I predict fingolimod will become a 1st-line treatment in Europe long
before 2019 in anticipation of it coming off patent. As always economics is the
trump card; if it wasn’t we wouldn’t have to resort to complex statistical
modeling and posts of this nature.”

“Another disruptor is cladribine; we didn’t include it in this analysis as it does not have a license as a DMT in MS. However, it has very good NEDA data and I suspect it would as efficacious, if not more efficacious, than fingolimod. This makes my recent post on the off-label use of cladribine in resource poor countries very pertinent to this post. I really wish we could resuscitate cladribine and get it a licensed as an alternative option for the treatment of RRMS. Oral cladribine has so many positive attributes; it is orally administered as short courses, it is an induction therapy, it ideal treatment for woman wanting to start, or extend their families, side effects are low, it is well tolerated, adherence is really not an issue and there is no secondary autoimmunity. Even the short-term malignancy scare appears to have disappeared although it is likely that long-term risks will remain an issue. The big issues are infection risks and persistent lymphopaenia; these are really problems across many DMTs and we know how to handle them. I hope Merck-Serono or a White Knight is reading this post.”

“When reading and assimilating information from this post, please note my conflicts of
interest below. I may be biased, but the data is what it is.” 

Epub: Nixon et al. No Evidence of Disease Activity: Indirect Comparisons of Oral Therapies for the Treatment of Relapsing-Remitting Multiple Sclerosis. Adv Ther. 2014 Nov 21. 

INTRODUCTION: No head-to-head trials have compared the efficacy of the oral therapies, fingolimod, dimethyl fumarate and teriflunomide, in multiple sclerosis. Statistical modeling approaches, which control for differences in patient characteristics, can improve indirect comparisons of the efficacy of these therapies.

METHODS: No evidence of disease activity (NEDA) was evaluated as the proportion of MSers free from relapses and 3-month confirmed disability progression (clinical composite), free from gadolinium-enhancing T1 lesions and new or newly enlarged T2 lesions (magnetic resonance imaging composite), or free from all disease measures (overall composite). For each measure, the efficacy of fingolimod was estimated by analyzing individual patient data from fingolimod phase 3 trials using methodologies from studies of other oral therapies. These data were then used to build binomial regression models, which adjusted for differences in baseline characteristics between the studies. Models predicted the indirect relative risk of achieving NEDA status for fingolimod versus dimethyl fumarate or teriflunomide in an average patient from their respective phase 3 trials.

RESULTS: The estimated relative risks of achieving NEDA status for fingolimod versus placebo in a pooled fingolimod trial population were numerically greater (i.e., fingolimod more efficacious) than the estimated relative risks for dimethyl fumarate or teriflunomide versus placebo in each respective trial population. In indirect comparisons, the predicted relative risks for all composite measures were better for fingolimod than comparator when tested against the trial populations of those treated with dimethyl fumarate (relative risk, clinical: 1.21 [95% confidence interval 1.06-1.39]; overall: 1.67 [1.08-2.57]), teriflunomide 7 mg (clinical: 1.22 [1.02-1.46]; overall: 2.01 [1.38-2.93]) and teriflunomide 14 mg (clinical: 1.14 [0.96-1.36]; overall: 1.61 [1.12-2.31]).

CONCLUSION: Our modeling approach suggests that fingolimod therapy results in a higher probability of NEDA than dimethyl fumarate and teriflunomide therapy when phase 3 trial data are indirectly compared and differences between trials are adjusted for.

CoI: multiple. I am a co-author on this paper; it came out of a project presented at the Global Fingolimod Advisory Board (GFAB); I am a sitting member on this board. 

About the author

Prof G

Professor of Neurology, Barts & The London. MS & Preventive Neurology thinker, blogger, runner, vegetable gardener, husband, father, cook and wine & food lover.


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  • Part of me regrets not starting at Tysabri when I was offered it first-line (with the condition I'm JC-) , how does Tysabri compare to Gilenya in achieving NEDA ?

    • Nothing is stopping us doing cladribine. However, the analysis had to be done for a particular country's regulators and they did not ask for a comparison with cladribine. We also had to make the call that the paper would be unwieldy with another agent. i can ask them to run the analysis; however, time is money and they are always working on something so they may not be allocated time for this work.

    • It has already been done! Not – as far as published data go – for NEDA, but for relapse rate & deteriorating disability:

      Indirect comparisons of oral fingolimod versus natalizumab and cladribine for the treatment of relapsing–remitting multiple sclerosis based on data from FREEDOMS, AFFIRM and CLARITY
      R. Nixon, B. Eckert, G. Cutter, F. Mercier, G. Francis, L. Kappos (Basel, CH; East Hanover, Birmingham, Cambridge, US)

      Results reported suggest there is little to choose between Fingolimod, Cladribine and Natalizumab in terms of relapse rate, whilst Natalizumab seemed to have slight advantage over the other two in preventing disability.

      As of today the study has not been published, perhaps not surprising given significant conflicts of interest involved.

  • ProfG
    Based on your presentation it suggests that fingolimod is 1.7X better than Tecfidera and 1.7X better than Aubagio. So does this mean that Aubagio is just as good asTecfidera? However, it is being claimed that Tecfidera is much better than Aubagio. Is this correct or do you just think this is selective marketing buy the manufacturers of Tecfidera?

  • Perhaps a more interesting problem to address with modern statistics tools would be to identify clusters of responders/non-responders in the trial data. Is the apparent difference in the efficacy of fingolimod vs. dimethylfumarate vs. teriflunomide due to greater fraction of responders, ore higher efficacy in "responder sub-population", or higher efficacy across the board?

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