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Biochemical recurrence (BCR) surrogacy for clinical outcomes after radiotherapy for adenocarcinoma of the prostate (BCRSCRAP): A meta-analysis from MARCAP Consortium.

2023· article· en· W4324136998 on OpenAlexaff
Soumyajit Roy, Tahmineh Romero, Allison Steigler, James W. Denham, David Joseph, Jeff M. Michalski, Felix Y. Feng, M. Bolla, Theo M. de Reijke, P. Maingon, Matthew R. Sydes, David P. Dearnaley, Luca Incrocci, Wilma D. Heemsbergen, Abdenour Nabid, Luís Souhami, A. Zapatero, Yilun Sun, Daniel E. Spratt, Amar U. Kishan

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcGill University Health CentreCentre Hospitalier Universitaire de Sherbrooke
Fundersnot available
KeywordsMedicineCensoring (clinical trials)Hazard ratioProstate cancerProportional hazards modelClinical endpointOncologyMeta-analysisInternal medicineBiochemical recurrenceRandomized controlled trialRadiation therapySurvival analysisClinical trialConfidence intervalCancerProstatectomy

Abstract

fetched live from OpenAlex

391 Background: Event-free survival, a PSA-driven endpoint, was shown to not be surrogate endpoint for overall survival (OS) in the ICECAP two-stage meta-analytic approach. However, time to biochemical recurrence (TTBCR) in NRG/RTOG 9202 met Prentice criteria for surrogacy. We performed an individual patient data (IPD) meta-analysis of 11 randomized controlled trials evaluating RT dose escalation, ADT use, and adjuvant ADT prolongation to evaluate the surrogacy of time to BCR (TTBCR), censoring for non-prostate cancer deaths, using both approaches to evaluate surrogacy. Methods: This individual patient level meta-analysis was performed using data from the MARCAP consortium, and 11 radiotherapy trials were included. TTBCR was defined as time to developing a BCR or experiencing prostate cancer-specific mortality (PCSM), with censoring at time of other-cause death or loss to follow-up. Landmark analyses were used to test the Prentice criteria for surrogacy. For patient level correlation between TTBCR and OS, we applied a bivariate Copula model to estimate the Kendall’s τ. For trial level correlation of the treatment effect on TTBCR and true endpoints, a weighted linear regression model was applied between the effects of treatment (natural log of hazard ratio [log-HR]) on OS versus TTBCR using a weightage that was inverse variance of BCR log-HR estimate. Results: Based on Prentice criteria, BCR at the landmark time point of 48 months was associated with increased risk of mortality in trials that compared treatment intensification with adjuvant ADT prolongation (HR 2.18 [95% CI 1.95-2.42]), the addition of ADT (HR 1.38 [1.25-1.54]), and RT dose escalation (HR 2.12 [1.83-2.46]) on uni- and multi-variable analyses. At the patient level, there was a low to moderate level correlation between BCR and OS with Kendall’s τ of 0.34 and a R2 of 0.55 for correlation of treatment effect on TTBCR and OS. At the trial level, there was a poor correlation between treatment effect on TTBCR and OS (R2=0.16). Conclusions: This IPD meta-analysis demonstrates that while BCR is prognostic, it is not a surrogate endpoint for OS in localized prostate cancer for patients treated with a diverse array of radiotherapeutic strategies. This highlights the importance of other cause mortality in prostate cancer. Our results highlight the differences in interpretability of Prentice criteria and the two-stage meta-analytic approach and suitability of endpoints for clinical trial design.

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)0.035
metaresearch head score (Gemma)0.049
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 score0.035
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.049
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0090.054
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.299
GPT teacher head0.518
Teacher spread0.220 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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