ArticleAmerican journal of epidemiology2023
Evaluating Model Specification When Using the Parametric G-Formula in the Presence of Censoring.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The effect of hypothetical interventions to limit weight gain on incident type 2 diabetes mellitus among people with HIV initiating antiretroviral therapy in the United States, 2012-2021.American journal of epidemiology · 2026Article
- Active Monitoring versus Surgical Excision for Atypical Ductal Hyperplasia.Cancer prevention research (Philadelphia, Pa.) · 2026Observational
- Cardiovascular disease risk under low-fat, Mediterranean, and American Heart Association diets: a target trial emulation in United States adults.The American journal of clinical nutrition · 2026Article
- Best (but oft forgotten) practices: Applying the target trial framework in nutrition research.The American journal of clinical nutrition · 2026Article
- Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.Epidemiology (Cambridge, Mass.) · 2026Article
- Guideline-based physical activity and health-related quality of life among prostate cancer survivors: a target trial emulation in the Health Professionals Follow-up Study.American journal of epidemiology · 2026Article
- Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings.Proceedings of machine learning research · 2026Article
- Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- Synthetic target trial emulation and predictive modeling of amylin-pathway therapies for obesity and type 2 diabetes.Metabolism open · 2025Article
- Effectiveness of sustained leisure-time physical activity strategies for obesity-related cancer prevention: an emulated target trial in a prospective US cohort.BMC medicine · 2025Article
- The dawn of target trials in intensive care: time to move beyond association is not causation.Intensive care medicine · 2025Article
- Estimating the Effects of Hypothetical AmbientEnvironmental health perspectives · 2025Article
- Evaluating recommendation-based dietary and physical activity strategies for prostate cancer prevention: a target trial emulation in the Health Professionals Follow-up Study.American journal of epidemiology · 2025Article
- Article
- Addressing treatment switching in the ALTA-1L trial with g-methods: exploring the impact of model specification.BMC medical research methodology · 2024Article
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6 authors.
Funding
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.
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