Assessing the risk-benefit profile of ramucirumab in patients with advanced solid tumors: A meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Ramucirumab is a widely used cancer drug having gained six regulatory approvals in various advanced solid tumors. Thus, assessing the risk-benefit profile of such a commonly used drug across multiple tumor types is necessary to inform clinical and reimbursement decisions. To objectively assess the risks and benefits of ramucirumab in patients with advanced solid tumors, we performed a systematic review and meta-analysis of published randomized controlled trials (RCTs). METHODS: A systematic search of PubMed, the Cochrane Library, Google Scholar and conference abstracts for all RCTs of ramucirumab in patients with advanced solid cancer was conducted according to the PRISMA guidelines. Data on treatment-related serious adverse events (SAEs), fatal adverse events (FAEs), primary endpoint, gains in median overall survival (OS), progression-free survival (PFS) with their hazard ratios (HR) and 95% confidence intervals (CIs) and quality of life (QOL) were extracted from each RCT. Summary relative risks (RR) with 95% CI for SAEs and FAEs were calculated by pooling data across the RCTs using random-effects model. Treatment benefit was evaluated descriptively in terms of median and HR with 95% CI of OS and PFS gains as well as improvements in QOL. ESMO-Magnitude of Clinical Benefit Scale (MCBS), a validated tool, was used to objectively quantify clinical benefit of ramucirumab in approved settings. FINDINGS: Ten RCTs met our inclusion criteria. Compared with the control arm, the use of ramucirumab was associated with a significantly increased risk of developing SAE (RR 1.13, 95% CI 1.05-1.21, incidence 37.5% v 33.5%). The increase in risk of FAE (RR 1.41, 95% CI 0.96-2.07, incidence 1.8% v 1.3%) was not statistically significant. Using the ESMO MCBS tool, clinical benefit with ramucirumab was not substantial in any indication, five of six approvals scoring a "negligible benefit" score of 1 or 2. QOL was either not reported (30%) or not improved (70%) in these RCTs. INTERPRETATION: In this meta-analysis of RCTs, use of ramucirumab in patients with advanced cancer was associated with increased risk of treatment related serious and possibly fatal adverse events but the magnitude of clinical benefit from ramucirumab was mostly negligible with no trial reporting an improvement in QOL. These relative risks and benefits should be considered in clinical and regulatory decision making. FUNDING: None.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".