Revisiting the g-null Paradox. Issue 1 (January 2022)

• Record Type: Journal Article
• Authors: McGrath, Sean; Young, Jessica G.; Hernán, Miguel A.
• Is Part Of: Epidemiology. Volume 33:Issue 1(2022)
• Publication Date: 2022-01
• Subjects: Causal inference -- g-null paradox -- Model misspecification -- Parametric g-formula; Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000001431
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : The (noniterative conditional expectation) parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often-cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed in some settings consistent with the conditions that motivate its use (i.e., when identifiability conditions hold and measured time-varying confounders are affected by past treatment). Many users of the parametric g-formula acknowledge the g-null paradox as a limitation when reporting results but still require clarity on its meaning and implications. Here, we revisit the g-null paradox to clarify its role in causal inference studies. In doing so, we present analytic examples and a simulation-based illustration of the bias of parametric g-formula estimates under the conditions associated with this paradox. Our results highlight the importance of avoiding overly parsimonious models for the components of the g-formula when using this method. Abstract : Supplemental Digital Content is available in the text.