Placebo Response in Rheumatoid Arthritis Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: Understanding the placebo response is critical to interpreting treatment efficacy, particularly for agents with a ceiling to their therapeutic effect, where an increasing placebo response makes it harder to detect potential benefit. The objective of this study is to assess the change in placebo responses over time in rheumatoid arthritis (RA) randomized placebo-controlled trials (RCT) for drug licensing authorization. METHODS: The Cochrane Controlled Trials Register database was searched to identify RCT of biological or targeted synthetic disease-modifying antirheumatic drugs (DMARD) in RA. Studies were excluded if patients were conventional synthetic DMARD (csDMARD)-naive, not receiving background csDMARD therapy, or were biologic experienced. Metaregression model was used to evaluate changes in American College of Rheumatology (ACR) 20, ACR50, and ACR70 treatment response over time. RESULTS: There were 32 trials in total: anti-tumor necrosis factor therapy (n = 15), tocilizumab (n = 4), abatacept (n = 2), rituximab (n = 2), and Janus kinase inhibitors (n = 9). From 1999 to 2018, there was no significant trend in the age or sex of patients in the placebo arm. Disease duration, swollen joint count, and 28-joint count Disease Activity Score using erythrocyte sedimentation rate at baseline all significantly declined over time. There was a statistically significant increase in placebo ACR50 and ACR70 responses (ACR50 β = 0.41, 95% CI 0.09-0.74, p = 0.01; ACR70 β = 0.18, 95% CI 0.04-0.31, p = 0.01) that remained significant after controlling for potential confounders. CONCLUSION: There has been a rise in the placebo response in RA clinical trials over the last 2 decades. Shifting RA phenotype, changes in trial design, and expectation bias are possible explanations for this phenomenon. This observation has important implications when evaluating newer novel agents against established therapies.
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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 | 0.328 | 0.422 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".