Comparative effectiveness of nivolumab (NIVO) relative to standard of care (SOC) for advanced/metastatic (adv/met) gastric or gastroesophageal junction cancer (GC/GEJC): A simulated treatment comparison (STC).
Bibliographic record
Abstract
128 Background: The prognosis of adv/met GC/GEJC among patients receiving third and later lines (L) of therapy is poor, and effective treatment options are limited. The study objective was to estimate the relative effect of NIVO versus SOC for overall survival (OS), in the US among adv/met GC/GEJC patients who received ≥ 3L therapy. Methods: A STC was performed using individual patient data from the single-arm CheckMate 032 (CM032) trial, and the Flatiron Health (FH) database. Eligible patients had adv/met GC/GEJC and received NIVO (CM032) or SOC (FH) as ≥ 3L therapy; all patients met CM032 eligibility criteria. A regression model of OS was fit to CM032 data using prognostic factors and treatment effect modifiers identified through a systematic literature review. The regression model was used to predict OS for NIVO, using Flatiron patient characteristics as covariates, and to estimate the expected outcome if NIVO had been available in the Flatiron population. The observed and predicted OS for NIVO was compared against the observed OS for SOC to generate naïve and adjusted hazard ratio comparisons of NIVO vs SOC. Results: In total, 42 and 43 patients were included from CM032 and Flatiron, respectively. In the Cox model, 19 prognostic factors were considered and the final model adjusted for 6, based on data availability across the two sources: ECOG, alkaline phosphatase (ALP) and hemoglobin, sex, prior surgery, and tumor location. Median OS was 8.97 months in the NIVO group and 5.61 months in the SOC group. The STC adjustment yielded a hazard ratio of 0.66 (95% CI: 0.41 to 1.06) for NIVO vs SOC compared to the naïve estimate of 0.64 (0.40 to 1.03). Sensitivity analyses confirm this result. Conclusions: In the absence of head-to-head data, this study suggests that NIVO may confer a benefit in terms of OS versus SOC for patients with GC/GEJC in ≥ 3L therapy in the US setting. Despite the inherent limitations of using non-randomized comparisons of clinical trial data and real-world evidence, these findings provide insight into the potential benefit of novel agents such as NIVO.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".