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P1093: MATCHING-ADJUSTED INDIRECT COMPARISON (MAIC) OF ZANUBRUTINIB (ZANU) VERSUS IBRUTINIB (IBRU) IN RELAPSED/REFRACTORY MARGINAL ZONE LYMPHOMA (R/R MZL)

2023· article· en· W4385706381 on OpenAlexaff
Catherine Thiéblemont, Kaijun Wang, Sam Keeping, Ina Zhang, Keri Yang, Boxiong Tang, Leyla Mohseninejad

Bibliographic record

VenueHemaSphere · 2023
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrecision Nanosystems (Canada)
Fundersnot available
KeywordsMedicinePercentileInternal medicineProgression-free survivalOncologyOverall survivalStatisticsMathematics

Abstract

fetched live from OpenAlex

Topic: 36. Ethics and health economics Background: ZANU is a Bruton tyrosine kinase inhibitor (BTKi) that has been evaluated for the treatment of R/R MZL in two phase 2, single-arm trials (MAGNOLIA, n=66, NCT03846427; BGB-3111-AU-003, n=20, NCT02343120). At 28 and 35 months of study follow-up in MAGNOLIA and BGB-3111-AU-003, respectively, median progression-free survival (PFS) and overall survival (OS) were not reached. IBRU, a first-generation BTKi, has also been evaluated for R/R MZL in a phase 2, single-arm trial (PCYC-1121, n=60 [Noy et al., Blood 2017; Noy et al., Blood Adv 2020]). Aims: Here, we conducted an unanchored MAIC to estimate the comparative efficacy of ZANU versus IBRU in R/R MZL. Methods: The MAIC utilized study-level data from PCYC-1121 and pooled individual patient-level data from MAGNOLIA and BGB-3111-AU-003. A logistic propensity score model was used to estimate weights for patients in the ZANU trials so that weighted mean baseline characteristics matched those in PCYC-1121. The following characteristics were identified as key prognostic factors and included in the base case propensity score model: number of prior lines of therapy, MZL subtype, response to prior therapy, and age. A sensitivity analysis was conducted including additional characteristics (B symptoms, time since last therapy, prior anti-CD20 therapy, bulky disease [>5cm], and lactate dehydrogenase above normal). Comparisons were conducted for OS, PFS, and objective response rate (ORR) by independent review committee using weighted statistical models with relative treatment effects presented as hazard ratios (HRs), odds ratios (ORs), and 95% confidence intervals (CIs). Results: After applying weights estimated from the base case propensity score model, the effective sample size (ESS) for ZANU was 68 (Table). Compared with IBRU, ZANU significantly reduced the risk of progression (HR 0.38; 95% CI 0.21–0.69, P=0.001) and was associated with a higher ORR (OR 2.37; 95% CI: 1.13–4.96, P=0.022). OS was comparable for ZANU and IBRU, which is consistent with expected survival for indolent lymphomas. The sensitivity analysis accounting for additional prognostic factors suggested the two treatments were comparable across all outcomes, owing in part to the low ESS (24) for ZANU associated with the expanded model. A leave-one-out analysis showed improved PFS (HR 0.33–0.45) for ZANU when excluding B symptoms, time since last therapy, or bulky disease from the expanded model. Summary/Conclusion: This MAIC demonstrated ORR and PFS benefits for ZANU versus IBRU in R/R MZL.Keywords: Indolent non-Hodgkin’s lymphoma, Marginal zone, relapsed/refractory, Tyrosine kinase inhibitor

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.050
GPT teacher head0.308
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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