Matching-adjusted indirect comparison of talazoparib plus enzalutamide versus abiraterone acetate and docetaxel in mCRPC
Bibliographic record
Abstract
Aims The absence of direct comparisons between talazoparib plus enzalutamide (TALA+ENZA) and current standard of care hinders evaluating their relative efficacy for first-line (1 L) metastatic castration resistant prostate cancer (mCRPC). This study aimed to compare TALA+ENZA (TALAPRO-2) to abiraterone acetate plus prednisone (AAP) (COU-AA-302) and docetaxel (TAX 327) using a matching-adjusted indirect treatment comparison (MAIC).Methods A systematic literature review using the Ovid® interface was performed to identify relevant evidence. Patient-level data from TALAPRO-2 and published data from COU-AA-302 and TAX 327 were used to match populations on clinically relevant confounders. The MAICs were conducted for radiographic progression-free survival (rPFS), overall survival (OS), objective response rate (ORR), along with additional efficacy outcomes.Results In all-comers, TALA+ENZA statistically significantly prolonged rPFS (HR: 0.256; 95% confidence interval [CI]: 0.183, 0.359; p < 0.0001), OS (HR: 0.557; 0.405, 0.766; p = 0.0003), and improved ORR (OR: 3.924; 2.017, 7.634; p = 0001) versus AAP. In all-comers, TALA+ENZA significantly prolonged OS (HR: 0.446; 0.316, 0.631; p < 0.0001) and improved ORR (OR: 13.081; 5.757, 29.721; p < 0.0001) versus docetaxel. All other efficacy outcomes statistically favored TALA+ENZA.Conclusions These results suggest TALA+ENZA improves clinical outcomes relative to AAP and docetaxel in the 1 L mCRPC all-comers population.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".