The importance of considering differences in study and patient characteristics before undertaking indirect treatment comparisons: a case study of siponimod for secondary progressive multiple sclerosis
Bibliographic record
Abstract
Background: Indirect treatment comparisons (ITCs) provide valuable evidence on comparative efficacy where head-to-head clinical trials do not exist; however, differences in patient populations may introduce bias. Therefore, it is essential to assess between-trial heterogeneity to determine the suitability of synthesizing ITC results. We provide an illustrative case study in multiple sclerosis (MS) where we assess the feasibility of conducting ITCs between siponimod and interferon beta-1b (IFN β-1b) and between siponimod and ocrelizumab.Methods: We assessed the feasibility of conducting ITCs using standard unadjusted methods (e.g. Bucher or network meta-analysis [NMA]) as well as matching-adjusted indirect comparisons (MAICs) using individual patient data (IPD) from the siponimod (EXPAND) trial, based on guidance from NICE. Time to confirmed disability progression (CDP) at 3 or 6 months was assessed.Results: Bucher ITCs and NMAs, which rely on summary-level data, were not able to account for important cross-trial differences. Comparisons between siponimod and IFN β-1b were feasible using MAIC; the HRs (95% CI) for CDP-6 and CDP-3 were 0.55 (0.33–0.91) and 0.82 (0.42–1.63), respectively. ITCs were not feasible between siponimod and ocrelizumab because study designs and patient populations were too dissimilar to conduct a reliable ITC.Conclusions: This study highlights the importance of conducting a detailed feasibility assessment before undertaking ITCs to illuminate when excessive between-trial heterogeneity would cause biased results. MAIC was performed for siponimod and IFN β-1b in the absence of a head-to-head trial and was considered a more valid approach than a traditional ITC to examine comparative effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".