A comparison of biologics in first-line advanced colorectal cancer: A Bayesian network meta-analysis of EGFR inhibitors and bevacizumab.
Bibliographic record
Abstract
543 Background: Adding bevacizumab (B) or EGFR inhibitors (E) to chemotherapy have improved outcomes when compared to chemotherapy (chemo) alone in the first-line treatment of mCRC, however it is unclear which of these combinations is optimal. As the 2 RCTs presented to date were not powered to detect overall survival (OS) benefits and have shown conflicting OS results, a meta-analysis may be beneficial. Methods: We conducted a systematic review of RCTs comparing (1) E + chemo vs. B + chemo (2) E + chemo vs. chemo only, or (3) B + chemo vs. chemo only, using MEDLINE, Embase, Cochrane Central, and ASCO abstracts up to June 2013 with Cochrane methodology. Data on PFS and OS were extracted using the Parmar method. For RCTs involving E, only the K-ras WT data was included. The patient characteristics and outcomes of the reference arms of the RCTs were examined to assess for heterogeneity. Bayesian pairwise and network meta-analyses (NMA) were conducted to estimate the direct, indirect and combined PFS and OS hazard ratios comparing E to B using WinBUGs. Results: Seventeen RCTs (8,048 patients) were identified; 15 of them contained extractable data for quantitative analysis. Direct pairwise meta-analyses (2 RCTs) comparing E vs. B showed that PFS HR=1.00 (95% credible regions (CR): 0.86-1.17) and OS HR=0.76 (95% CR: 0.63-0.92) in favour of E. Indirect comparisons of E vs. B (through the intermediate of chemo only: 5 RCTs comparing E + chemo vs. chemo only, 8 RCTs comparing B + chemo vs. chemo only) showed that PFS HR=1.31 (95% CR: 0.98-1.85) and OS HR=1.06 (95% CR: 0.93-1.22). Combining direct and indirect comparisons with NMA (15 RCTs) showed that the PFS HR=1.10 (95% CR: 1.00-1.21) (trend in favour of B) and OS HR=0.95 (95% CR: 0.85-1.06). Conclusions: The results of direct pairwise meta-analysis, dominated mostly by FIRE-3, suggested E improves OS without PFS benefits when compared to B. However, the results from indirect or combined NMA synthesizing all relevant data from the existing literature did not confirm those findings. The findings of FIRE-3 may be due to chance or trial specific reasons. The results of the upcoming CALGB 80405 will provide further direct evidence to help refine these estimates.
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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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.051 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".