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

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.

This is a pre-copyedited, author-produced PDF of an article accepted for publication in American Journal of Epidemiology following peer review. The version of record [Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring. American Journal of Epidemiology 192, 11 p1887-1895 (2023)] is available online at: https://doi.org/10.1093/aje/kwad143.

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Work Title Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring
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Open Access
Creators
  1. Yu Han Chiu
  2. Lan Wen
  3. Sean McGrath
  4. Roger Logan
  5. Issa J. Dahabreh
  6. Miguel A. Hernán
Keyword
  1. Noniterative conditional expectation parametric g-formula
  2. Censoring
  3. Model misspecification
  4. Inverse probability weighting
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. American Journal of Epidemiology
Publication Date June 20, 2023
Publisher Identifier (DOI)
  1. https://doi.org/10.1093/aje/kwad143
Deposited April 21, 2024

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Version 1
published

  • Created
  • Added AJE_00341-2022_clean_5.11.2023.pdf
  • Added Creator Yu Han Chiu
  • Added Creator Lan Wen
  • Added Creator Sean McGrath
  • Added Creator Roger Logan
  • Added Creator Issa J. Dahabreh
  • Added Creator Miguel A. Hernán
  • Published
  • Updated
  • Updated Keyword, Publication Date Show Changes
    Keyword
    • Noniterative conditional expectation parametric g-formula, Censoring, Model misspecification, Inverse probability weighting
    Publication Date
    • 2023-11-01
    • 2023-06-20