Indirect Treatment Comparison of Liso-Cel vs. Salvage Chemotherapy in Diffuse Large B-Cell Lymphoma: TRANSCEND vs. SCHOLAR-1
Bibliographic record
Abstract
Most patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) have exhausted their treatment options and are deemed palliative. CD19-directed chimeric antigen receptor (CAR) T-cell therapy has recently been introduced as a new option for these patients. Lisocabtagene maraleucel (liso-cel) is an investigational CAR T-cell therapy that has shown promising activity in this setting. We used an unanchored matching-adjusted indirect comparison (MAIC) methodology to compare liso-cel, using individual patient-level data from the TRANSCEND NHL 001 (TRANSCEND; NCT02631044) trial, to salvage chemotherapy, using summary-level data from the SCHOLAR-1 study, for the treatment of patients with R/R LBCL. Standardized mean differences were used to evaluate imbalances between the TRANSCEND and SCHOLAR-1 studies. MAIC was conducted to determine the relative efficacy of liso-cel vs. salvage chemotherapy with regard to overall survival, complete response rate, and objective response rate. For all efficacy outcomes assessed, comparisons of clinical factors before MAIC showed that five of seven baseline characteristics were similar between the TRANSCEND and SCHOLAR-1 studies; however, age and R/R to last therapy status differed between studies, thus requiring matching and adjusting to ensure the validity of this analysis. The base case analyses demonstrated a significantly lower risk of mortality (hazard ratio, 0.5; 95% confidence interval [CI] 0.4-0.6; p < 0.001) with significantly higher rates of complete response (odds ratio, 12.9; 95% CI 8.0-20.7) and objective response (odds ratio, 7.0; 95% CI 4.6-10.5) for patients treated with liso-cel than patients treated with salvage chemotherapy. MAIC comparisons demonstrated favorable efficacy for liso-cel compared with salvage chemotherapy in the treatment of patients with R/R LBCL.Trial Registration ClinicalTrials.gov identifier: NCT02631044.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".