Population-adjusted network meta-analyses (NMA) to evaluate the efficacy of treatment alternatives for metastatic hormone-sensitive prostate cancer (mHSPC).
Bibliographic record
Abstract
267 Background: Existing NMA in mHSPC compare treatments assuming homogeneity in treatment effect modifiers across study populations. However, evidence suggests that these effects vary in mHSPC. This study employs the recent methods of multilevel network meta-regression (ML-NMR) and network meta interpolation (NMI) to adjust for population differences and predict relative effect estimates for an ARASENS-like target population. Methods: We used a systematic literature review to identify studies for our NMA on overall survival (OS) in mHSPC. We applied ML-NMR to adjust for imbalances in treatment effect modifiers by integrating individual patient data (IPD) regression from the ARASENS trial across the covariate distributions of comparator studies. The IPD was reconstructed from Kaplan-Meier curves for aggregate data studies, incorporating covariates age, ECOG performance status, Gleason score, and disease volume. Utilizing the same set of variables, NMI was employed to analyze subgroup data from comparator studies using reported hazard ratios. Results: Twelve studies were identified for inclusion in our analyses. The ML-NMR demonstrated a significant benefit for the darolutamide (daro) triplet compared to enzalutamide + ADT, apalutamide + ADT, abiraterone + ADT, and the abiraterone triplet in the ARASENS-like population. However, in the NMI analysis, the daro triplet was significantly favored over abiraterone + ADT when compared to other commonly utilized alternatives (Table). A key strength of the ML-NMR over NMI is that it only requires baseline characteristics to be reported, rather than subgroups which were relatively poorly reported or not powered to detect statistically significant differences. The NMI method suffered due to incomplete subgroup data, with full data for effect modifiers available in 8/12 studies. Conclusions: Both ML-NMR and NMI analyses support triplet benefit with ADT + docetaxel + daro in improving OS for patients with mHSPC. The evidence from ML-NMR is particularly robust, benefiting from the better covariate data and the attendant decrease in uncertainty. Hazard ratio from ML-NMR and NMI. Daro + docetaxel + ADT versus ML-NMR: OSHR (95% CrI) NMI: OSHR (95% CrI) SNA+ADT 0.28 (0.17, 0.46)* 0.36 (0.23, 0.56)* ADT 0.43 (0.32, 0.60)* 0.51 (0.39, 0.65)* Docetaxel + ADT 0.57 (0.42, 0.77)* 0.68 (0.57, 0.80)* Enzalutamide + ADT 0.64 (0.44, 0.96)* 0.80 (0.57, 1.11) Apalutamide + ADT 0.68 (0.46, 1.00)** 0.75 (0.55, 1.03) Abiraterone + ADT 0.65 (0.45, 0.92)* 0.65 (0.52, 0.81)* Abiraterone acetate + docetaxel + ADT 0.66 (0.47, 0.93)* 0.89 (0.59, 1.34) CrI: credible interval, SNA: standard nonsteroidal antiandrogen, ADT: androgen deprivation therapy, *upper CrI did not cross the line of no effect, **rounding, upper CrI limit treated as below 1.00.
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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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.030 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".