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Updated: Apr 25, 2026

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Published on: October 23, 2020
The parametric g-formula for time-to-event data: intuition and a worked example
Alexander P Keil1, Jessie K Edwards, David B Richardson
1From the aDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC; and bDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.
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.
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