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Network meta-analysis (NMA) to assess comparative efficacy of lenvatinib plus pembrolizumab compared with other first-line treatments for management of advanced renal cell carcinoma (aRCC).

2024· article· en· W4391303168 on OpenAlexaff
Viktor Grünwald, Eric Winquist, Avivit Peer, Manuela Schmidinger, Giuseppe Procopio, Philippe Barthélémy, Jae‐Lyun Lee, Sarah Rudman, Ananth Kadambi, Anuja Pandey, Binod Neupane, Kyle Fahrbach, Sneha Purushotham, Michael Jones, Sonya Egodage, Ananth Kashyap, W. Hauck, Janice Pan

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsLenvatinibMedicinePembrolizumabRenal cell carcinomaMeta-analysisOncologyInternal medicineHepatocellular carcinomaCancerImmunotherapySorafenib

Abstract

fetched live from OpenAlex

482 Background: The CLEAR trial showed statistically significant improvements in overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and complete response (CR) in subjects treated with lenvatinib plus pembrolizumab (L+P) vs. sunitinib. We conducted an indirect treatment comparison to investigate the comparative efficacy of L+P vs. other first-line (1L) treatments in aRCC. Methods: A systematic literature review identified 24 randomized controlled trials evaluating 22 interventions in 1L treatments for aRCC. Bayesian NMAs were conducted to evaluate comparative efficacy outcomes for intention to treat (ITT) and the intermediate-/poor risk population, based on the CLEAR trial final data cutoff (31st Jul 2022). Results: L+P had a >70% probability of providing greater OS benefit than 8 of the 12 comparators; the benefit was statistically significant against 2 treatments (interferon α-2a: hazard ratio [HR] 0.65; 95% credible interval [CrI] 0.48–0.88 and sunitinib: 0.79; 0.63–0.99). For PFS (assessed under United States Food and Drug Administration censoring rules), L+P showed a >75% probability of providing greater benefit over all available comparators, including numerical, but not statistically significant advantage over immunoncology (IO) therapies nivolumab+ipilimumab (N+I), avelumab+axitinib (A+A), nivolumab+cabozantinib (N+C) and pembrolizumab + axitinib (P+A). The benefit was significant for 13 out of 18 comparators—relative efficacy estimates ranged from HR=0.18 (95% CrI 0.08–0.40) for placebo to HR=0.51 (95% CrI 0.29–0.89) for atezolizumab + bevacizumab. For response outcomes, L+P demonstrated >90% probability of greater ORR compared with all available comparators; the benefit was statistically significant against 9 of 12 comparators—the relative odds ratio ranged from 1.85 (95% CrI 1.22–2.81) for P+A to 38.47 (95% CrI 11.94–180.19) for placebo. L+P showed statistically significant ORR benefit against IO therapies N+I and P+A, and numerical, but not statistically significant advantage over A+A and N+C. Greater than 80% probability of CR benefit was observed across 13 of 14 comparators, with statistical significance achieved against 8 comparators. L+P showed numerical, but not statistically significant advantage over all IO comparators. Comparison of the ITT population and intermediate-/poor subgroup results indicated that the benefit of L+P on PFS against comparators seen in the ITT population was generally maintained in the subgroup for those comparisons that were still feasible. Conclusions: The NMA results show that combination therapy with L+P provides a comparable OS, and a trend of improvement in PFS and response outcomes, compared with most current global standard of care IO therapies for treatment-naïve patients with aRCC.

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)0.024
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.048
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0110.036
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.410
GPT teacher head0.493
Teacher spread0.083 · 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 designMeta-analysis
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
Published2024
Admission routes1
Has abstractyes

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