ArticleJournal of causal inference2014
Targeted Maximum Likelihood Estimation for Dynamic and Static Longitudinal Marginal Structural Working Models.
Article in Journal of causal inference, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed.
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- Multiply-robust estimator of cumulative incidence function difference for right-censored competing risks data.BMC medical research methodology · 2026Article
- Long-term dynamic effect of body mass index on adverse cardiovascular outcomes with targeted maximum likelihood estimation method: result from the KNOW-CKD study.Scientific reports · 2026Article
- Nonparametric Causal Inference for Optogenetics: Sequential Excursion Effects for Dynamic Regimes.Journal of the American Statistical Association · 2026Article
- What Are We Estimating? Revisiting Standard Nutritional Models Through the Target Trial Framework.American journal of epidemiology · 2026Article
- Deep Doubly Debiased Longitudinal Effect Estimation with ICE G-Computation.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- Adaptive Sparsening and Smoothing of the Treatment Model for Longitudinal Causal Inference Using Outcome-Adaptive LASSO and Marginal Fused LASSO.Statistics in medicine · 2026Article
- Effects Among the Affected.Statistics in medicine · 2025Article
- Statistical Methods to Adjust for Treatment Switching in Real-World Clinical Studies: A Scoping Review and Descriptive Comparison.Clinical pharmacology and therapeutics · 2025Article
- Is Checking for Sequential Positivity Violations Getting You Down? Try sPoRT!Epidemiology (Cambridge, Mass.) · 2025Article
- Inference procedures in sequential trial emulation with survival outcomes: Comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife.Statistical methods in medical research · 2025Article
- Type 2 diabetes, sodium-glucose cotransporter-2 inhibitors and cardiovascular outcomes: real world evidence versus a randomised clinical trial.Cardiovascular diabetology · 2025Article
- GLP-1RA comparative effectiveness against dementia onset relative to other antidiabetic medications in a large, multi-site cohort of patients with type 2 diabetes.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Observational
- Causal estimation of time-varying treatments in observational studies: a scoping review of methods, applications, and missing data practices.BMC medical research methodology · 2025Article
- A Bayesian Approach to the G-Formula via Iterative Conditional Regression.Statistics in medicine · 2025Article
- A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding.Lifetime data analysis · 2025Article
- Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.Statistics in medicine · 2025Article
- Assessing the impact of insulin resistance trajectories on cardiovascular disease risk using longitudinal targeted maximum likelihood estimation.Cardiovascular diabetology · 2025Article
- Identification and Estimation of the Average Causal Effects Under Dietary Substitution Strategies.Statistics in medicine · 2025Article
- A Bayesian latent class approach to causal inference with longitudinal data.Statistical methods in medical research · 2025Article
21 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
This paper describes a targeted maximum likelihood estimator (TMLE) for the parameters of longitudinal static and dynamic marginal structural models. We consider a longitudinal data structure consisting of baseline covariates, time-dependent intervention nodes, intermediate time-dependent covariates, and a possibly time-dependent outcome. The intervention nodes at each time point can include a binary treatment as well as a right-censoring indicator. Given a class of dynamic or static interventions, a marginal structural model is used to model the mean of the intervention-specific counterfactual outcome as a function of the intervention, time point, and possibly a subset of baseline covariates. Because the true shape of this function is rarely known, the marginal structural model is used as a working model. The causal quantity of interest is defined as the projection of the true function onto this working model. Iterated conditional expectation double robust estimators for marginal structural model parameters were previously proposed by Robins (2000, 2002) and Bang and Robins (2005). Here we build on this work and present a pooled TMLE for the parameters of marginal structural working models. We compare this pooled estimator to a stratified TMLE (Schnitzer et al. 2014) that is based on estimating the intervention-specific mean separately for each intervention of interest. The performance of the pooled TMLE is compared to the performance of the stratified TMLE and the performance of inverse probability weighted (IPW) estimators using simulations. Concepts are illustrated using an example in which the aim is to estimate the causal effect of delayed switch following immunological failure of first line antiretroviral therapy among HIV-infected patients. Data from the International Epidemiological Databases to Evaluate AIDS, Southern Africa are analyzed to investigate this question using both TML and IPW estimators. Our results demonstrate practical advantages of the pooled TMLE over an IPW estimator for working marginal structural models for survival, as well as cases in which the pooled TMLE is superior to its stratified counterpart.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.