Cost-effectiveness analysis of metformin with enzalutamide in the metastatic castrate-resistant prostate cancer setting.
Bibliographic record
Abstract
INTRODUCTION: Enzalutamide (Enza) is an effective treatment for metastatic castrate-resistant prostate cancer (mCPRC). However, Enza is not cost-effective (CE) at willingness to pay (WTP) thresholds from $0-$125 000/quality adjusted life years (QALYs) and is therefore a strain on valuable health care dollars. Metformin (Met) is inexpensive (~$8.00/month) and is thought to improve prostate cancer specific and overall survival compared to those not taking Met. We hypothesized that there must be an added effect Met could provide that would make Enza CE thereby alleviating this financial strain on government health care budgets. MATERIALS AND METHODS: We constructed a Markov model and performed a threshold analysis to narrow in on the added effect needed to make such a combination therapy cost-effective at various WTP thresholds. RESULTS: At a WTP threshold of $50 000/QALY Enza + Met is unlikely to be CE unless it increases Enza's efficacy by more than 30%. At a WTP threshold of $100 000, Enza + Met could be CE barring Met adds 18.73% to the efficacy of Enza. CONCLUSIONS: Enza + Met is unlikely to be CE at WTP thresholds less than $100 000/QALY; these results make sense because a therapy that is not CE at these WTP thresholds by itself is unlikely to be CE with an adjuvant therapy that keep a patient on such a treatment for even longer. Finally, our model suggests that the mCRPC setting is not the optimal place to trial adding Met as the relative costs are high and utility values low.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".