Model Selection, Confounder Control, and Marginal Structural Models

2004/10/13 by Marshall M. Joffe, Thomas R Ten Have, Harold I. Feldman +1 · 1 voice · 1 citation
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · doi:10.1198/000313004x5824

openalex publication_date 2004/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

In traditional regression modeling, to control for confounding by a variable one must include it in the structural part of the statistical model. Marginal structural models are a flexible new set of causal models. The estimation methods used to estimate model parameters use weighting to control for confounding; this allows more flexibility in choosing covariates for inclusion in the structural model and allows the model to more precisely reflect the scientific questions of interest. An important example of this is in multicenter observational studies where there is confounding by cluster. We illustrate these points with data from a study of surgery to provide vascular access for hemodialysis and a study comparing different timings for coronary angioplasty.

Citations

• Association, Causation, And Marginal Structural Models
• A Theorem on the Manipulability of Redundant Serial Kinematic Chains
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Longitudinal data analysis using generalized linear models
• The central role of the propensity score in observational studies for causal effects

Cited by

• Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

Discussions

• Hey #EpiSky Do any of you have access to this paper and can you send me a copy of the pdf? doi.org/10.1198/000313004X5824 [bsky, 1 points, 1 comments]

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