Matching-adjusted indirect treatment comparison of siponimod and other disease modifying treatments in secondary progressive multiple sclerosis
Bibliographic record
Abstract
Background: Siponimod, interferon beta-1a (IFNβ-1a), IFNβ-1b and natalizumab have been evaluated as treatments for secondary progressive multiple sclerosis (SPMS) in separate randomized controlled trials (RCTs), but not head-to-head. These trials included heterogeneous patient populations, which limits the use of standard network meta-analysis (NMA) for indirect treatment comparison (ITC) of relative efficacy. Matching-adjusted indirect comparison (MAIC) aims to correct these cross-trial differences. We compared siponimod to other disease modifying treatments (DMTs) in SPMS using MAIC.Methods: Individual patient data (IPD) were available for siponimod (EXPAND), while only published summary data were available for IFNβ-1a (Nordic Study, SPECTRIMS, IMPACT), IFNβ-1b (North American Study, European Study) and natalizumab (ASCEND). MAICs were conducted between siponimod and the other DMTs by re-weighting patients in EXPAND based on logistic regression.Results: Siponimod was determined to be statistically significantly more effective for the outcome of time to 6 month confirmed disability progression (CDP) compared with 22 µg IFNβ-1a and 250 µg IFNβ-1b, and for the outcome of time to CDP-3 compared with 60 µg IFNβ-1a. Siponimod was numerically but not statistically superior for CDP in all other comparisons. For annualized relapse rate (ARR), with the exception of natalizumab, siponimod was numerically but not statistically superior to all comparators.Conclusions: EXPAND provides evidence of the efficacy of siponimod compared with placebo, and these MAICs complement this by demonstrating improved efficacy of siponimod relative to DMTs. Siponimod offers a significant therapeutic advance that may slow disease progression compared to other DMTs in an EXPAND-like population with secondary progressive disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".