[Mediation-information] Mediation-information Digest, Vol 25, Issue 3

Tue Aug 26 20:56:12 CEST 2014

Hi,

In the model (education level --> economic position --> aging well) you should include all the variables that potentially confound the association between education level and economic position, education level and ageing well, and economic position and ageing well. 

Best wishes,
Mashhood
---
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 3
> To: mediation-information at lists.r-forge.r-project.org
> Date: Tue, 26 Aug 2014 19:03:02 +0200
>
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> Today's Topics:
>
> 1. Re: Mediation-information Digest, Vol 25, Issue 2
> (Mashhood Sheikh)
> 2. Causally dependent mediators (Angel Rodriguez)
> 3. Multilevel analysis for complex surveys (Angel Rodriguez)
> 4. Re: Multilevel analysis for complex surveys (Kentaro Hirose)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 26 Aug 2014 14:01:06 +0100
> From: Mashhood Sheikh <senor_massao at hotmail.com>
> To: "mediation-information at lists.r-forge.r-project.org"
> <mediation-information at lists.r-forge.r-project.org>
> Subject: Re: [Mediation-information] Mediation-information Digest, Vol
> 25, Issue 2
> Message-ID: <DUB121-W31834D20CCD1580F15D6DF7DC0 at phx.gbl>
> Content-Type: text/plain; charset="iso-8859-1"
>
> 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?
>
> ---
> Mashhood Ahmed Sheikh
> Research Fellow,
> Department of Community Medicine,?
> Faculty of Health,
> University of Troms?,
> 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
>>
>> Send Mediation-information mailing list submissions to
>> mediation-information at lists.r-forge.r-project.org
>>
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>>
>> or, via email, send a message with subject or body 'help' to
>> mediation-information-request at lists.r-forge.r-project.org
>>
>> You can reach the person managing the list at
>> mediation-information-owner at lists.r-forge.r-project.org
>>
>> When replying, please edit your Subject line so it is more specific
>> than "Re: Contents of Mediation-information digest..."
>>
>>
>> Today's Topics:
>>
>> 1. Causally dependent mediators (Angel Rodriguez)
>>
>>
>> ----------------------------------------------------------------------
>>
>> 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
>> Message-ID:
>> <8564BCD7D26E0D40872F1A132C8BBB250258B21B at MATIAEXCH.matiaf.local>
>> Content-Type: text/plain; charset="iso-8859-1"
>>
>>
>> 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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>> ****************************************************
>
>
> ------------------------------
>
> Message: 2
> Date: Tue, 26 Aug 2014 18:24:37 +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
> Message-ID:
> <8564BCD7D26E0D40872F1A132C8BBB250258B225 at MATIAEXCH.matiaf.local>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Thank you, Mashhood, for your answer.
>
> If I understand correctly, you recommend I run two models:
>
> infant deprivation --> education level --> aging well (plus age, gender, region)
>
> education level --> economic position --> aging well (plus age, gender, region)
>
>
> Because the effects of infant deprivation are considered in the first model, there is no need to include it as a confounder in the second model.
>
> Is that correct?
>
> Angel Rodriguez-Laso
>
>
> -----Mensaje original-----
> De: mediation-information-bounces at lists.r-forge.r-project.org en nombre de Mashhood Sheikh
> Enviado el: mar 26/08/2014 15:01
> Para: mediation-information at lists.r-forge.r-project.org
> Asunto: Re: [Mediation-information] Mediation-information Digest, Vol 25,Issue 2
>
> 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?
>
> ---
> Mashhood Ahmed Sheikh
> Research Fellow,
> Department of Community Medicine,?
> Faculty of Health,
> University of Troms?,
> 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
>>
>> Send Mediation-information mailing list submissions to
>> mediation-information at lists.r-forge.r-project.org
>>
>> To subscribe or unsubscribe via the World Wide Web, visit
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/mediation-information
>>
>> or, via email, send a message with subject or body 'help' to
>> mediation-information-request at lists.r-forge.r-project.org
>>
>> You can reach the person managing the list at
>> mediation-information-owner at lists.r-forge.r-project.org
>>
>> When replying, please edit your Subject line so it is more specific
>> than "Re: Contents of Mediation-information digest..."
>>
>>
>> Today's Topics:
>>
>> 1. Causally dependent mediators (Angel Rodriguez)
>>
>>
>> ----------------------------------------------------------------------
>>
>> 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
>> Message-ID:
>> <8564BCD7D26E0D40872F1A132C8BBB250258B21B at MATIAEXCH.matiaf.local>
>> Content-Type: text/plain; charset="iso-8859-1"
>>
>>
>> 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
>>
>>
>>
>>
>> -------------- next part --------------
>> An HTML attachment was scrubbed...
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>>
>> ------------------------------
>>
>> _______________________________________________
>> Mediation-information mailing list
>> Mediation-information at lists.r-forge.r-project.org
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/mediation-information
>>
>> End of Mediation-information Digest, Vol 25, Issue 2
>> ****************************************************
>
> _______________________________________________
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> ------------------------------
>
> Message: 3
> Date: Tue, 26 Aug 2014 18:49:49 +0200
> From: "Angel Rodriguez" <angel.rodriguez at matiainstituto.net>
> To: <mediation-information at lists.r-forge.r-project.org>
> Subject: [Mediation-information] Multilevel analysis for complex
> surveys
> Message-ID:
> <8564BCD7D26E0D40872F1A132C8BBB250258B226 at MATIAEXCH.matiaf.local>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear subscribers,
>
> I want to carry out a mediation analysis with variables collected in a survey (for more information, see the thread "causally dependent mediators"). It was a complex survey with strata and individuals clustered in census sections. I do not have census section variables, but I want the models to be aware that individuals are not independent because they're grouped in census sections.
>
> My first question is if I should use mediation with glmer.
>
> In that case, my second question would be about the syntax to carry out this. In the example in Tingley's "mediation: R Package for Causal Mediation Analysis", the proposed models are:
>
>
> R> library(lme4)
> R> set.seed(2014)
> R> med.fit <- glmer(attachment ~ catholic + gender + income + pared + (1|SCH_ID),
> + family = binomial(link = "logit"), data = student)
> R> out.fit <- glmer(fight ~ catholic*attachment +
> + gender + income + pared + (1 + attachment|SCH_ID),
> + family = binomial(link = "logit"), data = student)
>
> I don't understand why in med.fit the term related to the clustering units is (1|SCH_ID), while (1 + attachment|SCH_ID) is the term in the out.fit.
>
> Thank you very much,
>
> Angel Rodriguez-Laso
> Research Project Manager
> Matia Instituto Gerontologico
> -------------- next part --------------
> An HTML attachment was scrubbed...
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>
> ------------------------------
>
> Message: 4
> Date: Tue, 26 Aug 2014 17:02:52 +0000
> From: Kentaro Hirose <hirose at Princeton.EDU>
> To: Angel Rodriguez <angel.rodriguez at matiainstituto.net>,
> "mediation-information at r-forge.wu-wien.ac.at"
> <mediation-information at r-forge.wu-wien.ac.at>
> Subject: Re: [Mediation-information] Multilevel analysis for complex
> surveys
> Message-ID:
> <7F71CA3EDBFDB742900C47C9596B90233909ADDF at CSGMBX201W.pu.win.princeton.edu>
>
> Content-Type: text/plain; charset="us-ascii"
>
> Hi Angel,
>
>> I don't understand why in med.fit the term related to the clustering units is (1|SCH_ID), while (1 + attachment|SCH_ID) is the term in the out.fit.
>
> This is just fitting a varying-intercept model for the mediator and a varying-intercept-and-slope model for the outcome. You could also fit two varying-intercept models for both the mediator and outcome. And similarly, you could also fit two varying-intercept-and-slope models for both the mediator and outcome. It's all up to your theory.
>
>
>> My first question is if I should use mediation with glmer.
>
> If your dependent variable is binary and you want to use a multilevel model, then you should use glmer.
>
>
> Best,
> Kentaro Hirose
>
> Postdoctoral Fellow
> Department of Politics
> Princeton University
>
>
>
> ________________________________________
> From: mediation-information-bounces at r-forge.wu-wien.ac.at [mediation-information-bounces at r-forge.wu-wien.ac.at] on behalf of Angel Rodriguez [angel.rodriguez at matiainstituto.net]
> Sent: Tuesday, August 26, 2014 12:49 PM
> To: mediation-information at r-forge.wu-wien.ac.at
> Subject: [Mediation-information] Multilevel analysis for complex surveys
>
> Dear subscribers,
>
> I want to carry out a mediation analysis with variables collected in a survey (for more information, see the thread "causally dependent mediators"). It was a complex survey with strata and individuals clustered in census sections. I do not have census section variables, but I want the models to be aware that individuals are not independent because they're grouped in census sections.
>
> My first question is if I should use mediation with glmer.
>
> In that case, my second question would be about the syntax to carry out this. In the example in Tingley's "mediation: R Package for Causal Mediation Analysis", the proposed models are:
>
>
> R> library(lme4)
> R> set.seed(2014)
> R> med.fit <- glmer(attachment ~ catholic + gender + income + pared + (1|SCH_ID),
> + family = binomial(link = "logit"), data = student)
> R> out.fit <- glmer(fight ~ catholic*attachment +
> + gender + income + pared + (1 + attachment|SCH_ID),
> + family = binomial(link = "logit"), data = student)
>
> I don't understand why in med.fit the term related to the clustering units is (1|SCH_ID), while (1 + attachment|SCH_ID) is the term in the out.fit.
>
> Thank you very much,
>
> Angel Rodriguez-Laso
> Research Project Manager
> Matia Instituto Gerontologico
>
>
> ------------------------------
>
> _______________________________________________
> Mediation-information mailing list
> Mediation-information at lists.r-forge.r-project.org
> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/mediation-information
>
> End of Mediation-information Digest, Vol 25, Issue 3
> ****************************************************