Pharmacoepidemiology

Evaluating Metformin Strategies for Cancer Prevention: A Target Trial Emulation Using Electronic Health Records

Dickerman, Barbra A.^a,b; García-Albéniz, Xabier^a,b,c; Logan, Roger W.^a,b; Denaxas, Spiros^d,e,f; Hernán, Miguel A.^a,b,g

Author Information

From the ^aCAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA

^bDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA

^cRTI Health Solutions, Barcelona, Spain

^dInstitute of Health Informatics Research, University College London, London, UK

^eHealth Data Research UK (HDR UK) London, University College London, London, UK

^fThe Alan Turing Institute, London, UK

^gDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Submitted November 26, 2022; accepted May 4, 2023

This study is based in part on data from the CPRD obtained under license from the UK Medicines and Healthcare Products Regulatory Agency. The data are provided by patients and collected by the UK National Health Service as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. Because electronic health records are classified as sensitive data by the UK Data Protection Act, information governance restrictions (to protect patient confidentiality) prevent data sharing via public deposition. Data are available with approval through the individual constituent entities controlling access to the data. Specifically, the primary care data can be requested via application to the CPRD (https://www.cprd.com).

B.A.D., X.G.-A., S.D., and M.A.H. conceived the overall study. All authors contributed to the design and analysis. B.A.D. analyzed the data. R.W.L. provided key input in processing data from the database. All authors contributed to the interpretation of the results. B.A.D. wrote the first draft of the article, and all authors reviewed, revised, and approved the final version of the article.

Supported by grant R00 CA248335 to B.A.D. from the National Institutes of Health.

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Correspondence: Barbra A. Dickerman, Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, 8th Floor, Boston, MA 02115. E-mail: [email protected].

Epidemiology 34(5):p 690-699, September 2023. | DOI: 10.1097/EDE.0000000000001626

Abstract

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, and 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.

Article Keywords

Keywords
Cancer prevention, Causal inference, Comparative effectiveness, Electronic health records, Intention-to-treat analysis, Inverse-probability weighting, Per-protocol analysis, Pharmacoepidemiology, Target trial

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Evaluating Metformin Strategies for Cancer Prevention: A Target Trial Emulation Using Electronic Health Records