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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".