title: The parametric g-formula for time-to-event data: intuition and a worked example.
abstract: 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.
authors.affiliation: From the aDepartment of Epidemiology, University of North Carolina, Chapel Hill, NC; and bDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Québec, Canada.
authors.forename: Alexander P
authors.initials: AP
authors.lastname: Keil
authors.forename: Jessie K
authors.initials: JK
authors.lastname: Edwards
authors.forename: David B
authors.initials: DB
authors.lastname: Richardson
authors.forename: Ashley I
authors.initials: AI
authors.lastname: Naimi
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Cole
mesh_terms: D000328:Adult; D000465:Algorithms; D016026:Bone Marrow Transplantation / Q000401:mortality*; D002423:Cause of Death; D015986:Confounding Factors, Epidemiologic; D004812:Epidemiologic Methods; D005260:Female; D006086:Graft vs Host Disease / Q000453:epidemiology / Q000517:prevention & control*; D006801:Humans; D008297:Male; D009010:Monte Carlo Method; D015337:Multicenter Studies as Topic; D011336:Probability