Comparing outcomes from clinical studies of oral disease-modifying therapies (dimethyl fumarate, fingolimod, and teriflunomide) in relapsing MS: Assessing absolute differences using a number needed to treat analysis
Bibliographic record
Abstract
Dimethyl fumarate (DMF), fingolimod, and teriflunomide are oral disease-modifying therapies (DMTs) indicated for the treatment of relapsing-remitting multiple sclerosis. Despite well-established limitations of cross-trial comparisons, DMTs are still frequently compared in terms of relative reductions in specific endpoints, most commonly annualized relapse rate. Consideration of absolute risk reduction and number needed to treat (NNT) provides an alternative approach to assess the magnitude of treatment effect and can provide valuable additional information on therapeutic gain. Using data from pivotal studies of DMF (DEFINE, NCT00420212; CONFIRM, NCT00451451), fingolimod (FREEDOMS, NCT00289978; FREEDOMS II, NCT00355134), and teriflunomide (TEMSO, NCT00134563; TOWER, NCT00751881), we calculated NNTs to prevent any relapse, more severe relapses (such as those leading to hospitalization or requiring intravenous corticosteroids), and disability worsening. Higher relative reductions were reported for DMF and fingolimod vs placebo on overall relapse and relapses requiring intravenous corticosteroids in both individual and pooled studies (pooled data unavailable for fingolimod). However, NNTs for each outcome were similar for DMF and teriflunomide, with marginally lower NNTs observed with fingolimod. By contrast, for relapses requiring hospitalization, relative reductions were higher and NNTs were substantially lower for teriflunomide compared with DMF. For fingolimod, there were inconsistent outcomes between the two studies for relapses requiring hospitalization; thus, comparative conclusions against DMF or teriflunomide cannot be clearly established. The risk of disability worsening was significantly reduced in both teriflunomide studies, but only in a single study for DMF (DEFINE) and fingolimod (FREEDOMS). NNTs to prevent one patient from experiencing disability worsening were similar in DEFINE, FREEDOMS, and TEMSO and TOWER but were higher in CONFIRM and FREEDOMS II. This NNT analysis demonstrates broadly comparable effects for DMF, fingolimod, and teriflunomide across key clinical outcomes. These observations are clinically relevant and may help to inform treatment decisions by providing additional information on therapeutic gain beyond informal assessments of relative reductions alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.073 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".