Model Selection, Confounder Control, and Marginal Structural Models: Review and New Applications

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...

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Bibliographic Details
Published in:The American statistician 2004-11, Vol.58 (4), p.272-279
Main Authors: Joffe, Marshall M, Ten Have, Thomas R, Feldman, Harold I, Kimmel, Stephen E
Format: Article
Language:English
Subjects:
Angioplasty
Applications
Causality
Dialysis
Estimating techniques
Estimation methods
Exact sciences and technology
Fistula
General topics
Hemodialysis
Heparin
Kidneys
Linear inference, regression
Logistic regression
Marginal analysis
Mathematics
Medical sciences
Observational studies
Parametric models
Percutaneous transluminal coronary angioplasty
Prediction
Predictions
Probability and statistics
Regression analysis
Sciences and techniques of general use
Statistical models
Statistics
Surgery
Surgical outcomes
Surgical specialties
Term weighting
Variables
Weighting
Citations: Items that this one cites
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Summary: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.
ISSN:0003-1305
1537-2731
DOI:10.1198/000313004X5824