Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

Journal: American journal of epidemiology
PMID:

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 the Clinical Practice Research Datalink, a UK primary-care database (1987-2018). We included 93 353 individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated use of metformin (n = 79 489) or a sulfonylurea (n = 13 864). In our cohort, the estimated overlap-weighted additive separable direct effect of metformin compared with sulfonylureas on cancer risk at 6 years was -1 percentage point (95% CI, -2.2 to 0.1), which is consistent with metformin's providing no direct protection against cancer incidence or substantial protection. The analysis faced 2 methodological challenges: (1) poor overlap and (2) precancer 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. This article is part of a Special Collection on Pharmacoepidemiology.

Authors

  • Shenbo Xu
    Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142, United States.
  • Bang Zheng
    Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142, United States.
  • Bowen Su
    Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142, United States.
  • Stan Neil Finkelstein
    Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02142, United States.
  • Roy Welsch
    MIT-IBM Watson AI Lab, Cambridge, MA, USA.
  • Kenney Ng
    Center for Computational Health, IBM Research, Yorktown Heights, NY, USA.
  • Zach Shahn
    MIT-IBM Watson AI Lab, Cambridge, MA, USA.