Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring

Yu Han Chiu, Lan Wen, Sean McGrath, Roger Logan, Issa J. Dahabreh, Miguel A. Hernán

Research output: Contribution to journalArticlepeer-review

Abstract

The noniterative conditional expectation (NICE) parametric g-formula can be used to estimate the causal effect of sustained treatment strategies. In addition to identifiability conditions, the validity of the NICE parametric g-formula generally requires the correct specification of models for time-varying outcomes, treatments, and confounders at each follow-up time point. An informal approach for evaluating model specification is to compare the observed distributions of the outcome, treatments, and confounders with their parametric g-formula estimates under the “natural course.” In the presence of loss to follow-up, however, the observed and natural-course risks can differ even if the identifiability conditions of the parametric g-formula hold and there is no model misspecification. Here, we describe 2 approaches for evaluating model specification when using the parametric g-formula in the presence of censoring: 1) comparing factual risks estimated by the g-formula with nonparametric Kaplan-Meier estimates and 2) comparing natural-course risks estimated by inverse probability weighting with those estimated by the g-formula. We also describe how to correctly compute natural-course estimates of time-varying covariate means when using a computationally efficient g-formula algorithm. We evaluate the proposed methods via simulation and implement them to estimate the effects of dietary interventions in 2 cohort studies.

Original languageEnglish (US)
Pages (from-to)1887-1895
Number of pages9
JournalAmerican journal of epidemiology
Volume192
Issue number11
DOIs
StatePublished - Nov 1 2023

All Science Journal Classification (ASJC) codes

  • General Medicine

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