Metformin (Met): A cost-effective adjunct therapy with enzalutamide (Enza) for metastatic castrate-resistant prostate cancer (mCRPC)?
Bibliographic record
Abstract
340 Background: Several new therapies have changed the landscape of prostate cancer (PCa) treatment, primarily due to their effectiveness in treating patients with mCRPC. Enza has garnered much attention, but is relatively expensive (~$3175/month). Met is less expensive (~$8.00/month) and has been used for decades to treat patients with non-insulin dependent diabetes. Two recent large population-based studies of PCa have demonstrated that diabetics taking Met had improved PCa specific and overall survival compared to those not taking Met. As a result, we hypothesized that Met has the potential to be a cost-effective adjunct therapy to Enza, although it is not currently used as such. Methods: We constructed a Markov-based decision analytic model to compare the cost-effectiveness of Enza alone versus Enza combined with Met. Through expert elicitation, we assumed that adding Met to Enza increases its efficacy by 15%. All other costs, utilities, and transition probabilities were derived from existing literature or expert elicitation. Effectiveness was measured using quality-adjusted life years (QALYs). Costs and QALYs were considered over a lifetime horizon and discounted at 5% per annum. Cost-effectiveness was considered using a willingness to pay threshold of $50 000/QALY. Results: Adding Met to Enza increases expected lifetime costs per patient by $83 651, and improves the expected effectiveness of treatment by 3.74 QALYs, compared to Enza alone. The incremental cost-effectiveness ratio is $22 374/QALY. Accounting for parameter uncertainty, adding Met to Enza has a 72% probability of being cost-effective. Conclusions: Although Met is not currently used as an adjunct therapy to Enza, doing so would likely be cost-effective provided it is as effective as we have assumed in our model. Additionally, our results indicate that the combination of Enza and Met could be among the most cost effective interventions in oncology. However, given the uncertainty around the effectiveness of such an adjunct therapy, our results support the need for further clinical trials to provide more robust evidence of the effectiveness of such a combination therapy in clinical practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".