Optimizing oncology drug development: systematic review of 22 years of myeloma randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Although myeloma represents a key success story in oncology, some drugs have failed to meet primary endpoints in randomized controlled trials (RCTs), despite promising early phase activity. This analysis aimed to understand factors that increase the likelihood of meeting primary endpoints in myeloma RCTs. METHODS: Myeloma RCTs published through October 2023 were identified using MEDLINE, PubMed, Embase, and the Cochrane Registry. Studies were classified as head-to-head (substituting 1 regimen for another) or add-on (adding 1 drug to existing regimen). Trials were considered successful if they achieved statistical significance for primary outcomes. Logistic regression identified predictors of meeting trial endpoints. RESULTS: A total of 145 comparisons from 123 RCTs were included. Only 2 factors were independently associated with meeting primary endpoints in multivariate analysis. Higher median participant age was associated with lower odds of meeting the primary endpoint (odds ratio [OR] per 1-year increase = 0.90, 95% confidence interval [CI] = 0.83 to 0.98). Overall survival (OS) was the primary endpoint in 20 of 145 comparisons, of which 3 of 20 met their endpoint. Selecting OS as primary endpoint was associated with reduced likelihood of success compared with progression-free survival by 94% (OR = 0.06, 95% CI = 0.01 to 0.23). Head-to-head design was not associated with lower success rates than add-on design (OR = 0.59; 95% CI = 0.22 to 1.62). CONCLUSION: Two key factors predicted higher likelihood of meeting endpoints: younger patient age and primary endpoints other than OS. Although head-to-head design is considered riskier, it was not associated with decreased success. This analysis aims to better inform clinicians, industry, and regulators in myeloma drug development.
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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 | 0.031 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".