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Comparative Study
. 2013 Aug;20(4):197-200.
doi: 10.3109/09286586.2013.792939. Epub 2013 Jul 2.

Marginal structural models for comparing alternative treatment strategies in ophthalmology using observational data

Marshall M Joffe, Maxwell Pistilli, John H Kempen
Comparative Study

Marginal structural models for comparing alternative treatment strategies in ophthalmology using observational data

Marshall M Joffe et al. Ophthalmic Epidemiol. 2013 Aug.
No abstract available

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Figures

Figure 1
Figure 1
Causal diagrams depicting relationships among key variables, illustrating the issue of time-dependent confounding. a: Actual relationships in population, where the intermediate variable L is a time-dependent confounder. b: Relationships in the pseudo-population created by weighting (condition 2 is removed) A0: Initial treatment A1: Subsequent treatment L: Intermediate variable (disease activity) Y: Outcome (visual acuity) U: Common cause (possibly unmeasured) of L and Y Conditions 1 Covariate L is independent predictor of outcome Y because of 1a (effect of L on Y) and/or 1b (unmeasured common cause U of L and Y) 2 Covariate L influences subsequent treatment A1 3 Covariate L is influenced by earlier treatment A0.
See this image and copyright information in PMC

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