Assessing Comparative Outcomes from Teriflunomide and Dimethyl Fumarate Studies in Relapsing MS: Use of “Number Needed to Treat” Analysis (P3.245)
Bibliographic record
Abstract
OBJECTIVE: To compare the number needed to treat (NNT) to prevent relapse or disability progression (DP) in clinical trials with teriflunomide or dimethyl fumarate (DMF). BACKGROUND: Teriflunomide and DMF, oral therapies for relapsing-remitting MS, have demonstrated efficacy in clinical trials. Treatment effects are sometimes compared using relative reductions in endpoints from different studies. However, the outcomes may be affected by differences in disease severity among study populations or differences on very low event rates. NNT to prevent an event is an additional approach for comparisons between MS drugs. DESIGN/METHODS: In this post hoc analysis, NNTs were derived using data from studies with teriflunomide 14mg (TEMSO, NCT00134563; TOWER, NCT00751881) or DMF (DEFINE, NCT00420212; CONFIRM, NCT00451451) based on the inverse of absolute differences between treatment and placebo groups. RESULTS: Teriflunomide studies included some patients with progressive disease; patients in DEFINE had slightly lower Expanded Disability Status Scale scores. Teriflunomide and DMF significantly reduced risk of relapse (all studies). NNTs to prevent 1 relapse were similar across studies (5.9 [TEMSO], 5.6 [TOWER], 5.3 [DEFINE], 5.6 [CONFIRM]). Risk of DP sustained for 12 weeks was significantly reduced in TEMSO, TOWER, and DEFINE, but not CONFIRM. Corresponding NNTs to prevent DP were 13.8, 17.4, 10.8, and 30.2. Risk of relapse leading to hospitalization was significantly reduced in TEMSO and TOWER, but not in DEFINE and CONFIRM. Corresponding NNTs were lower in TEMSO (12.5) and TOWER (20) than in DEFINE (50) and CONFIRM (50). CONCLUSIONS: Using the NNT approach, a comparable effect size for teriflunomide and DMF on relapse was observed. Teriflunomide is the only approved oral DMT with significant outcomes on DP in two studies. NNTs to prevent DP were similar for both teriflunomide studies and DEFINE, but were higher for CONFIRM. Study Supported by: Genzyme, a Sanofi company.
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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 | 0.206 | 0.213 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".