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Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.
Xu, Shenbo; Zheng, Bang; Su, Bowen; Finkelstein, Stan Neil; Welsch, Roy; Ng, Kenney; Shahn, Zach.
Afiliação
  • Xu S; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
  • Zheng B; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
  • Su B; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
  • Finkelstein SN; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
  • Welsch R; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
  • Ng K; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
  • Shahn Z; Massachusetts Institute of Technology, Institute for Data, Systems, and Society.
Am J Epidemiol ; 2024 Jul 19.
Article em En | MEDLINE | ID: mdl-39030720
ABSTRACT
There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first line diabetes therapy using data from Clinical Practice Research Datalink, a U.K. primary care database (1987-2018). We included individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated metformin (N=93353) or a sulfonylurea (N=13864). In our cohort, the estimated overlap weighted additive separable direct effect of metformin compared to sulfonylureas on cancer risk at 6 years was -1% (.95 CI=-2.2%, 0.1%), which is consistent with metformin providing no direct protection against cancer incidence or substantial protection. The analysis faced two methodological challenges-poor overlap, and pre-cancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap weighted separable effects in addition to more traditional effect estimates.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article