Abiraterone vs. docetaxel for metastatic hormone-sensitive prostate cancer: A microsimulation model
Bibliographic record
Abstract
INTRODUCTION: Our aim was to determine whether androgen deprivation therapy (ADT) with abiraterone acetate (AA) or ADT with docetaxel chemotherapy (DC) resulted in improved quality-adjusted life years (QALYs) among men with de novo metastatic castration-sensitive prostate cancer (mCSPC) and the cost effectiveness of the preferred strategy using decision analytic techniques. METHODS: A microsimulation model with a lifetime time horizon was constructed. Our primary outcome was QALYs. Secondary outcomes included cost, incremental cost effectiveness ratio (ICER), unadjusted overall survival (OS), rates of second- and third-line therapy, and adverse events. A systematic literature review was used to generate probabilities and utilities to populate the model. The base case was a 65-year-old patient with de novo mCSPC. RESULTS: A total of 100 000 microsimulations were generated. Initial AA resulted in a gain of 0.45 QALYs compared to DC (3.36 vs. 2.91 QALYs) with an ICER of $276 251.82 per QALY gained with initial AA therapy. Median crude OS was 51 months with AA and 48 months with DC. Overall, 46.6% and 42.6% of patients received second-line therapy and 8.7% and 7.9% patients received third-line therapy in the AA and DC groups, respectively. Grade 3/4 adverse events were experienced in 17.6% of patients receiving initial AA and 22.3% of patients receiving initial DC. CONCLUSIONS: Although ADT with AA results in a gain in QALYs and crude OS compared to DC, AA therapy is not a cost-effective treatment strategy to apply uniformly to all patients. The availability of AA as a generic medication may help to close this gap. The ultimate choice should be based on patient and tumor factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".