A probabilistic threshold analysis of metformin (Met) with enzalutamide (Enza) to determine the cost and added efficacy needed to make such a combination theray cost-effective (CE).
Bibliographic record
Abstract
e577 Background: Enza has become known for its' effectiveness in treating patients with metastatic castrate-resistant prostate cancer. However, Enza is expensive (~$3175/month) and unlikely to be CE at any willingness to pay (WTP) threshold from $0-$125 000/quality-adjusted life years (QALYs). Met is inexpensive (~$8.00/month) and recently two large population-based studies of prostate cancer (PCa) demonstrated that diabetics taking Met had improved PCa specific and overall survival compared to those not taking Met. As a result we theorized that there must be a cost of Enza and a specific added effect Met could provide that would make Met a CE adjuvant therapy to Enza even though it is not currently used as such. Methods: We constructed a Markov-based decision analytic model and performed a probabilistic threshold analysis to narrow in on several combinations of costs of Enza and added efficacies needed to make Enza + Met a CE combination therapy at different WTP thresholds. Costs, utilities, and transition probabilities were derived from existing literature. Effectiveness was measured using QALYs. Costs and QALYs were considered over a lifetime horizon and discounted at 5% per annum. Results: At a WTP threshold of $50 000/QALY Enza + Met is unlikely to be CE no matter the cost decrement or added efficacy applied to the model. At a WTP threshold of $75 000/QALY Met is also unlikely to be CE unless the price of Enza could drop to $1934/month and Met could also add 1% to the efficacy of Enza. At Enza's current cost and a WTP threshold of $100 000, Enza + Met could be CE barring Met adds 0.621% to the number of patients still on treatment at 36 months. Conclusions: Enza + Met is unlikely to be CE at WTP thresholds of $50 000/QALY or $75 000/QALY unless there is a significant price drop for Enza; these results make sense because a therapy that is considered not CE at these WTP thresholds by itself is unlikely to be CE with a theoretical adjuvant therapy that would hold a patient on such a treatment for even longer. Our results show that the only way for Enza + Met to be CE is through the theoretical overall survival benefit of Met as the price of Enza is unlikely to drop any time soon.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 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) | 0.007 | 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".