Uncovering a bias in estimated treatment effects on PIRA in multiple sclerosis clinical trials
Bibliographic record
Abstract
BACKGROUND: Interest in progression independent of relapse activity (PIRA) as an endpoint in multiple sclerosis (MS) clinical trials is surging. However, established definitions of PIRA may produce biased treatment effect estimates in the presence of a treatment-induced relapse reduction. METHODS: We applied different definitions of PIRA to pooled data from the OPERA I/II clinical trials (clinicaltrials.gov identifiers: NCT01247324, NCT01412333). Treatment effects on PIRA according to different methods were quantified by hazard ratios (HRs) and risk ratios (RRs). Next, we evaluated the bias in each definition using synthetic Expanded Disability Status Scale (EDSS) data simulating a control and an experimental arm with varying treatment effects on relapses and on PIRA. We quantified the bias by comparing the estimated effect on PIRA with the known true effect. FINDINGS: The pooled OPERA I/II population included 1656 participants. Estimated treatment effects on PIRA varied from a non-significant HR of 0.83 (CI = 0.66-1.04) to an HR of 0.73 (CI = 0.59-0.90) depending on the definition used. Follow-up analyses on simulated data (n = 800 per arm) revealed an underestimation of the true treatment effect on PIRA when using established definitions, with increasing bias as treatment effect on relapses increased. Defining PIRA as complementary to relapse-associated worsening (RAW) provided a less biased and operationally simple alternative. INTERPRETATION: For clinical trials with PIRA as an endpoint, we suggest a "complementary" definition of PIRA, relying on accurate exclusion of RAW promoted by appropriate visit timing. FUNDING: Italian Ministry of University and Research.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".