Indirect comparisons of siponimod with fingolimod and ofatumumab in multiple sclerosis: assessing the feasibility of propensity score matching analyses
Bibliographic record
Abstract
Objective Head-to-head trials comparing siponimod with fingolimod or ofatumumab in patients with multiple sclerosis (MS) are lacking. Instead, the comparative efficacy of siponimod can be derived from indirect treatment comparisons (ITCs). We assessed the suitability of ITCs leveraging individual patient data from relevant phase III trials across different MS phenotypes.Methods One siponimod trial in patients with secondary progressive MS (SPMS), four fingolimod trials (three in relapsing-remitting MS [RRMS], and one in primary progressive MS [PPMS]), and two ofatumumab trials in relapsing MS (RMS) were considered. The suitability of ITCs was evaluated based on trial design, patient eligibility criteria, baseline patient characteristics, placebo response, and outcome definitions for each trial. Analyses deemed feasible were conducted using one-to-one propensity score matching (PSM).Results An ITC between siponimod in SPMS and either fingolimod in RRMS or ofatumumab in RMS was not feasible because of insufficient overlap in key patient characteristics (e.g. disability level and relapse history) and differences in placebo response. However, a comparison between siponimod in SPMS and fingolimod in PPMS was feasible because of sufficient overlap in eligibility criteria and baseline characteristics. One-to-one PSM demonstrated siponimod was favored relative to fingolimod for time to 6- and 3-month confirmed disability progression though not significantly different (hazard ratio 0.76 [95% confidence interval 0.48–1.20; p-value = .240] and hazard ratio 0.80 [95% confidence interval 0.52–1.22; p-value = .300], respectively).Conclusions For trials in MS, clinical phenotype is an important determinant of ITC feasibility. An ITC between siponimod in SPMS and either fingolimod in RRMS or ofatumumab in RMS was not feasible. The only feasible comparison was between siponimod in SPMS and fingolimod in PPMS.
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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 | 0.178 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".