Shifting Our Thinking About Uncommon Disease Trials: The Case of Methotrexate in Scleroderma
Bibliographic record
Abstract
OBJECTIVE: Randomized trials for uncommon diseases suffer from methodological challenges: difficulty in recruiting sufficient numbers of patients and low power to detect important treatment effects. Using traditional (frequentist) analysis, p values > 0.05 mean investigators are unable to reject the null hypothesis (of no treatment effect). The medical community often labels trials with p values > 0.05 as "negative." Our study demonstrates how Bayesian analysis conveys more relevant information to clinicians - using the example of methotrexate (MTX) in systemic sclerosis (SSc). METHODS: Data from 71 patients with diffuse SSc (n = 35 MTX, n = 36 placebo) in the trial were reanalyzed using Bayesian models. We examined 3 primary outcomes: modified Rodnan skin score (MRSS), University of California Los Angeles (UCLA) skin score, and physician global assessment of overall disease activity. Using noninformative prior probability distributions, the probability of beneficial treatment effects for each outcome and the probability of simultaneous benefit in outcomes were computed. RESULTS: The probability that treatment with MTX results in better mean outcomes than placebo was 94% for MRSS, 96% for UCLA skin score, and 88% for physician global assessment. There was 96% probability that at least 2 of 3 primary outcomes were better on treatment. CONCLUSION: Bayesian analysis of uncommon disease trials allows for more flexible and clinically relevant interpretations of the data. From the trial data, clinicians can infer that MTX has a high probability of beneficial effects on skin score and global assessment.
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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 | 0.597 | 0.719 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.022 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".