The parametric g-formula for time-to-event data: intuition and a worked example

Epidemiology. 2014 Nov;25(6):889-97. doi: 10.1097/EDE.0000000000000160.


Background: The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied.

Methods: We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding.

Results: Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula.

Conclusions: The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms
  • Bone Marrow Transplantation / mortality*
  • Cause of Death
  • Confounding Factors, Epidemiologic
  • Epidemiologic Methods*
  • Female
  • Graft vs Host Disease / epidemiology
  • Graft vs Host Disease / prevention & control*
  • Humans
  • Male
  • Monte Carlo Method
  • Multicenter Studies as Topic
  • Probability