Risk of Failure of a Clinical Drug Trial in Patients with Moderate to Severe Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We conducted a systematic review to determine the risk of drug failure in clinical testing with patients with moderate to severe rheumatoid arthritis (RA). METHODS: Therapies for RA were investigated by reviewing phase I to phase III studies conducted from December 1998 to March 2011. Clinical trial success rates were calculated and compared to industry standards. Trial failures were classified as either commercial or clinical failures. The exclusion criteria for drugs in this study: drugs that were started in phase I studies prior to January 1998 for this indication; or studies that enrolled patients who were methotrexate-naive and/or had failed biologic therapy. RESULTS: A search in clinicaltrials.gov and approved drugs for the indication yielded a total of 69 drugs that met the study criteria. The cumulative success rate was determined to be 16%, which is equivalent to the industry standard of 16%. For each phase, the frequency of clinical failures exceeded commercial failures. Clinical studies equally comprised investigations of small molecules and biological agents, but biologics seemed to exhibit a higher success rate overall. CONCLUSION: Clinical trial risk in RA with the 84% failure rate reported here is at par with industry performance and phase II success rate seems to be highly predictive of phase III success.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".