[Mediation-information] Mediation-information Digest, Vol 25, Issue 2

[Mashhood Sheikh]

In order to assess if the effect of education on ageing well is partly or completely mediated by economic position, you can run this model (adjusted for all potential confounders):

education level --> economic position --> aging well 

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Mashhood Ahmed Sheikh
Research Fellow,
Department of Community Medicine, 
Faculty of Health,
University of Tromsø,
Norway



> From: mediation-information-request at lists.r-forge.r-project.org
> Subject: Mediation-information Digest, Vol 25, Issue 2
> To: mediation-information at lists.r-forge.r-project.org
> Date: Tue, 26 Aug 2014 12:00:22 +0200
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> 1. Causally dependent mediators (Angel Rodriguez)
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> Message: 1
> Date: Tue, 26 Aug 2014 00:14:31 +0200
> From: "Angel Rodriguez" <angel.rodriguez at matiainstituto.net>
> To: <mediation-information at lists.r-forge.r-project.org>
> Subject: [Mediation-information] Causally dependent mediators
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> 
> Dear subscribers,
> 
> 
> I'm interested in using mediation for the following conceptual model applied to survey data:
> 
> infant deprivation (binary) --> education level (ordered categorical) --> economic position (continous) --> aging well (dichotomous outcome)
> 
> I assume that, apart from the mediation effects, each link has direct effects on the outcome.
> 
> I've read in "Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies" (Imai et al 2011), that studying education as the main mediator wouldn't be a problem, because the sequential ignorability assumption would be met due to the fact that the second mediator is downstream (figure 6d of that paper). That would be my first model.
> 
> What should I do to know if the effect of education is partly or completely mediated by economic position? I understand I cannot use economic position as a main mediator in the model deprivation --> economic position --> aging well, because there would be an upstream mediator (education), which violates the sequential ignorability. I should use multimed then (my second model): 
> 
> 
> R> Xnames <- c("age","gender", "region")
> R> set.seed(2014)
> R> m.med <- multimed(outcome = "agingwell", med.main = "economic", med.alt = "education",
> + treat = "deprivation", covariates = Xnames,
> + data = framing, sims = 1000)
> R> summary(m.med)
> 
> 
> If I get a large mediating effect of "education" in my first model and a negligible effect of "economic" in the second, could I conclude that all the mediating effect of "education" on the relationship between "deprivation" and aging well is driven by "economic"?
> 
> I suppose I cannot analyse multilevel data with multimed.
> 
> 
> 
> Thank you very much.
> 
> Angel Rodriguez-Laso
> Research Project Manager
> Matia Instituto Gerontol?gico
> 
> 
> 
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