Talazoparib plus enzalutamide versus olaparib plus abiraterone acetate and niraparib plus abiraterone acetate for metastatic castration-resistant prostate cancer: a matching-adjusted indirect comparison
Bibliographic record
Abstract
BACKGROUND: Without head-to-head trials between talazoparib+enzalutamide (TALA + ENZA), olaparib+abiraterone acetate (OLAP + AAP), and niraparib plus AAP (NIRA + AAP) the ability to evaluate their relative efficacy as first-line (1 L) treatment in metastatic castration-resistant prostate cancer (mCRPC) is limited. The objective of this study was to assess the relative efficacy between TALA + ENZA (TALAPRO-2) versus OLAP + AAP (PROpel) and NIRA + AAP (MAGNITUDE) in 1 L mCRPC via a matching-adjusted indirect treatment comparison (MAIC). METHODS: Patient-level data from TALAPRO-2 and published data from PROpel and MAGNITUDE were used. TALAPRO-2 data were reweighted to satisfy the eligibility criteria for PROpel and MAGNITUDE. Talazoparib (0.5 mg/day) plus enzalutamide (160 mg/day) was compared to olaparib (300 mg twice daily) plus abiraterone acetate (1000 mg/day) and niraparib (200 mg/day) plus abiraterone acetate (1000 mg/day). Hazard ratios (HRs) were calculated for radiographic progression-free survival (rPFS) and overall survival (OS), and odds ratios (ORs) for prostate-specific antigen (PSA) response and objective response rate (ORR). Additional efficacy outcomes were assessed. RESULTS: In all-comers, TALA + ENZA was statistically superior to OLAP + AAP for rPFS (HR: 0.727; 95% confidence interval [CI]: 0.565, 0.935) and PSA response (OR: 1.663; 1.101, 2.510), and numerically favored for OS (HR: 0.847; 0.667, 1.076) and ORR (OR: 1.109; 0.646, 1.903). In patients with homologous recombination repair mutations (HRRm), relative to NIRA + AAP, TALA + ENZA was statistically superior for rPFS (HR: 0.460; 0.280, 0.754), and numerically favored for OS (HR: 0.601; 0.347, 1.041) and ORR (OR: 1.524; 0.579, 4.016). CONCLUSIONS: Results suggest that TALA + ENZA may provide improvements in clinical outcomes relative to OLAP + AAP and NIRA + AAP in 1 L mCRPC; however, inherent limitations associated with the complexity of the analyses must be considered.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".