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Evaluating metformin strategies for cancer prevention
A target trial emulation using electronic health records
Dickerman, B. A., García-Albéniz, X., Logan, R. W., Denaxas, S., & Hernán, M. A. (2023). Evaluating metformin strategies for cancer prevention: A target trial emulation using electronic health records. Epidemiology, 34(5), 690-699. Advance online publication. https://doi.org/10.1097/EDE.0000000000001626
BACKGROUND: Metformin users appear to have a substantially lower risk of cancer than nonusers in many observational studies. These inverse associations may be explained by common flaws in observational analyses that can be avoided by explicitly emulating a target trial.
METHODS: We emulated target trials of metformin therapy and cancer risk using population-based linked electronic health records from the UK (2009-2016). We included individuals with diabetes, no history of cancer, no recent prescription for metformin or other glucose-lowering medication, and hemoglobin A1C (HbA1c) <64 mmol/mol (<8.0%). Outcomes included total cancer and 4 site-specific cancers (breast, colorectal, lung, prostate). We estimated risks using pooled logistic regression with adjustment for risk factors via inverse-probability weighting. We emulated a second target trial among individuals regardless of diabetes status. We compared our estimates with those obtained using previously applied analytic approaches.
RESULTS: Among individuals with diabetes, the estimated 6-year risk differences (metformin - no metformin) were -0.2% (95% CI: -1.6%, 1.3%) in the intention-to-treat analysis and 0.0% (95% CI: -2.1%, 2.3%) in the per-protocol analysis. The corresponding estimates for all site-specific cancers were close to zero. Among individuals regardless of diabetes status, these estimates were also close to zero and more precise. By contrast, previous analytic approaches yielded estimates that appeared strongly protective.
CONCLUSIONS: Our findings are consistent with the hypothesis that metformin therapy does not meaningfully influence cancer incidence. The findings highlight the importance of explicitly emulating a target trial to reduce bias in the effect estimates derived from observational analyses.