Matching-adjusted indirect comparison of acalabrutinib versus ibrutinib in relapsed/refractory mantle cell lymphoma
Bibliographic record
Abstract
OBJECTIVE: In the absence of head-to-head clinical trials, matching-adjusted indirect comparison (MAIC) was used to compare two Bruton tyrosine kinase inhibitors (BTKis) approved for the treatment of relapsed/refractory (R/R) mantle cell lymphoma (MCL). This analysis compares the efficacy and safety of acalabrutinib versus ibrutinib using a more mature dataset than a previously published MAIC. METHODS: Individual patient data from 122 patients treated with acalabrutinib in a phase 2 study were weighted to match aggregate baseline characteristics of patients pooled from three separate trials of ibrutinib. Patients were matched on Eastern Cooperative Oncology Group performance status, simplified Mantle Cell Lymphoma International Prognostic Index, lactate dehydrogenase, prior lines of therapy, tumor burden, and blastoid histology. Outcomes assessed included progression-free survival (PFS), overall survival (OS), and adverse events. RESULTS: = 0.35). Acalabrutinib was associated with an improved safety profile compared with ibrutinib, with statistically significantly lower rates of grade ≥3 atrial fibrillation and thrombocytopenia. CONCLUSIONS: This comparison of two BTKis used in the treatment of R/R MCL showed that PFS and OS risk was not statistically different between the treatments; however, acalabrutinib had an improved safety profile compared with ibrutinib.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".