Indirect treatment comparison (ITC) of abiraterone acetate (AA) plus prednisone (P) and docetaxel (DOC) on patient-reported outcomes (PROs) in metastatic castration-naïve prostate cancer (mCNPC).
Bibliographic record
Abstract
200 Background: AA + P added to androgen deprivation therapy (ADT) improved overall survival among newly diagnosed mCNPC patients (pts) with high-risk disease (HRD) vs placebos (PBOs) + ADT in the phase 3 LATITUDE study. Although ADT with or without chemotherapy is recommended in clinical guidelines as the mainstay of management for mCNPC, adding DOC to ADT does not consistently improve health-related quality of life (HRQoL). We performed an ITC to understand the relative impact of AA + P vs DOC on PROs in mCNPC pts. Methods: PROs were assessed using the Functional Assessment of Cancer Therapy-Prostate (FACT-P) and Brief Pain Inventory (BPI). Mean change from baseline (BL) was based on differences in FACT-P and BPI scores between active vs control arms in LATITUDE (intention-to-treat [ITT] population) and CHAARTED (available data included mCNPC pts with high-volume disease [HVD] and low-volume disease [LVD]). Higher FACT-P score indicates better outcome/function; lower BPI score indicates better outcome/less pain. The probability of AA + P being better than DOC at 3, 6, 9, and 12 mos after treatment was based on fixed-effects Bayesian network meta-analysis. Results: Benefits in PROs with AA + P vs DOC were observed from 3 mos and sustained at least 1 year after treatment. Bayesian probability of AA + P being the better treatment for PROs ranged from 92.3% to 100%. Conclusions: Results from a Bayesian ITC suggest that AA + P was superior to DOC in improving PROs for at least 1 year after initiating treatment in men with mCNPC. In the absence of head-to-head trials, these analyses can provide useful insights on the relative impact of treatment options on HRQoL in mCNPC pts. Clinical trial information: NCT01715285. [Table: see text]
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".