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Radiographic progression-free survival (rPFS) and time to radiographic progression (TTrP) as surrogate endpoints in docetaxel-naïve metastatic castrate resistant prostate cancer (mCRPC): A pooled analysis of COU-AA-302 and ACIS.

2023· article· en· W4324137016 on OpenAlexaff
Soumyajit Roy, Yilun Sun, Daniel E. Spratt, Scott C. Morgan, Thomas Kim, Julia Malone, Christopher J.D. Wallis, Amar U. Kishan, Fred Saad, Shawn Malone

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité de MontréalMount Sinai HospitalOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineDocetaxelPlaceboInternal medicineEnzalutamideProgression-free survivalOncologyProstate cancerPopulationClinical endpointProportional hazards modelRandomized controlled trialCancerOverall survivalPathology

Abstract

fetched live from OpenAlex

136 Background: rPFS is often used as an intermediate clinical endpoint (ICE) for overall survival (OS) in randomized trials in mCRPC. However, the current literature shows conflicting results on the surrogacy of rPFS for OS. Moreover, it remains unknown if TTrP, which does not consider death as an event, is an ICE for OS. We performed a combined analysis of COU-AA-302 and ACIS to determine if TTrP and rPFS can be used as ICE. Methods: In COU trial, docetaxel-naïve mCRPC patients were randomized to abiraterone (abi) versus placebo. In ACIS, a similar patient population was randomized to abi alone or abi with apalutamide (abi+apa). We applied weighted Cox regression models to evaluate the effect of treatment on TTrP and OS and used landmark analyses to determine the if the treatment effect on OS is mediated by that on radiographic progression. We estimated a semiparametric Spearman correlation between the ICE and OS at the patient level. We determined the trial level correlation of treatment effect on the ICE and OS in the 2 trials where each of them was subdivided into 9 pseudo-trial centers and then calculating the adjusted R2 between center level estimates of treatment effect for ICE and OS. The procedure of creating pseudo-trial centers was repeated 500 times and the presented R2 is the average across 500 repetitions after excluding those with negative association. Results: Overall, 2016 patients were eligible for this study – 1053 from COU and 963 from ACIS. Abi was associated with superior TTrP (HR 0.55 [95%CI 0.45-0.66]) and OS (HR 0.80 [0.70-0.92]). Similar results were seen with abi+apa (0.51 [0.41-0.64], 0.77 [0.65-0.91]). Radiographic progression was associated with significantly higher hazard of death in the state arrival extended Markov proportional hazard model (3.64 [1.54-8.62]) while longer TTrP was associated with reduced hazard of death (0.94 [0.93-0.95]). At the patient level, the correlation between TTrP & OS and rPFS & OS was 0.58 [0.54-0.63] and 0.68 [0.65-0.71], in the overall cohort. In the abi and abi+apa group, the correlation between TTrP and OS was 0.60 [0.53-0.66] and 0.73 [0.66-0.79] and that for rPFS and OS was 0.72 [0.67-0.75] and 0.79 [0.74-0.83], respectively. At the trial level, the treatment effect on rPFS & OS and TTrP & OS were correlated with average R2 of 0.84, 0.84, 0.85, and 0.86, respectively. The mean surrogate threshold effect over 500 permutations for HRrPFS and HRTTrP was 0.78 and 0.70 in ACIS and 0.54 and 0.45 in the COU-AA-302 trials, respectively. Conclusions: TTrP and rPFS were found to have significant association with OS in chemo-naïve mCRPC patients. We noted a modest to strong correlation between the treatment effect on both the ICE and OS at the trial level. Larger meta-analytic studies are needed to validate these findings.

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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)0.021
metaresearch head score (Gemma)0.014
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.018
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.486
Teacher spread0.401 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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