Metformin and Cancer: Solutions to a Real-World Evidence Failure
Bibliographic record
Abstract
The quest to repurpose metformin, an antidiabetes drug, as an agent for cancer prevention and treatment, which began in 2005 with an observational study that reported a reduction in cancer incidence among metformin users, generated extensive experimental, observational, and clinical research. Experimental studies revealed that metformin has anticancer effects via various pathways, potentially inhibiting cancer cell proliferation. Concurrently, multiple nonrandomized observational studies reported remarkable reductions in cancer incidence and outcomes with metformin use. However, these studies were shown, in 2012, to be affected by time-related biases, such as immortal time bias, which tend to greatly exaggerate the benefit of a drug. The observational studies that avoided these biases did not find an association. Subsequently, the randomized trials of metformin for the treatment of type 2 diabetes and as adjuvant therapy for the treatment of various cancers, advanced or metastatic, did not find reductions in cancer incidence or outcomes. Most recently, the largest phase 3 randomized trial of metformin as adjuvant therapy for breast cancer, which enrolled 3,649 women with a 5-year follow-up, found no benefit for disease-free survival or overall survival with metformin. This major failure of observational real-world evidence studies in correctly assessing the effects of metformin on cancer incidence and outcomes was caused by preventable biases which, surprisingly, are still prominent in 2022. Rigorous approaches for observational studies that emulate randomized trials, such as the incident and prevalent new-user designs along with propensity scores, avoid these biases and can provide more accurate real-world evidence for the repurposing of drugs such as metformin.
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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 | 0.341 | 0.469 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.017 | 0.040 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.021 | 0.042 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".