A comparison of rheumatoid arthritis and systemic lupus erythematosus trial design: a commentary on ways to improve the number of positive trials in SLE.
Bibliographic record
Abstract
OBJECTIVES: Recent systemic lupus erythematosus (SLE) randomised controlled trials (RCTs) were examined for potential design flaws and compared to rheumatoid arthritis (RA) RCT over the same time period to suggest modifications to SLE RCTs that could help improve the potential success rate of future SLE trials. METHODS: RA and SLE biologics RCTs published between 2005 and July 2013 were identified using PubMed. Inclusion criteria, study design, outcome measures, sample size calculations, patient baseline characteristics steroid use in the protocol and results were extracted and compared. RESULTS: All trials required active disease for enrolment. Twenty-two RA RCTs and eight SLE RCTs were included. All RA RCTs used either a partial or continuous measure of improvement. SLE RCTs used SLEDAI, BILAG, SLAM, SRI and BICLA. RA trials were larger (543 vs. 376 participants). Concomitant corticosteroid use was stable in 100% of RA trials while all SLE RCTs allowed dose tapering. RA trials were mostly in methotrexate or DMARD inadequate responders whereas SLE trials allowed for the presence or absence immunosuppressives within all trials. Sample sizes in RA were determined on a change in disease activity or proportion meeting a disease state. Positive trials were found in 100% of RA RCTs and 25% of SLE RCTs. CONCLUSIONS: The potential insensitivity of SLE disease activity indices to partial improvements may result in type II errors in SLE RCTs. Varying concomitant pharmacotherapy, especially corticosteroid use, in SLE may blunt observed treatment effects. Steroid tapering should be considered a trial outcome in isolation. More realistic sample size calculations are needed in SLE.
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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 | 0.417 | 0.770 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.016 | 0.006 |
| Research integrity | 0.041 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".