The parametric G-formula for time-to-event data: towards intuition with a worked example
Published Date:Nov 2014
Pubmed Central ID:PMC4310506
Funding:R01 AI100654/AI/NIAID NIH HHS/United States
R01 CA117841/CA/NCI NIH HHS/United States
R01-AI100654/AI/NIAID NIH HHS/United States
R01-CA117841/CA/NCI NIH HHS/United States
T32 ES007018/ES/NIEHS NIH HHS/United States
T42-OH008673-08/OH/NIOSH CDC HHS/United States
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.
We provide a simple introduction to the parametric g-formula and illustrate its application in 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.
Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula.
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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