[Mediation-information] Interaction between exposures?

[Mashhood Sheikh]

Dear Kosuke,

Thank you very much for the reply. Please, let me confirm if I understand it correctly. You mean making a four category variables out of the two binary exposure variables as:

Reference: where X1=0 AND X2=0
Dummy1: where X1=1 AND X2=0
Dummy2: where X1=0 AND X2=1
Dummy3: where X1=1 AND X2=1

This means I will be running three mediation models (3 dummy's against the reference), and will get three natural direct, natural indirect, and total effects? That is if all the three comparisons are important? Please confirm.

Is there any solution about estimating one direct/indirect/total effect in a situation like this?



Thankfully,
Mashhood
Institute of Community Medicine,
University of Tromsø,
Norway.



> From: kimai at Princeton.EDU
> To: senor_massao at hotmail.com
> CC: mediation-information at r-forge.wu-wien.ac.at
> Subject: Re: [Mediation-information] Interaction between exposures?
> Date: Sat, 30 Nov 2013 02:53:23 +0000
> 
> You can think of it as the four-category exposure. Now, the definition of indirect effects is a little bit different: in Y(t,M(t)) - Y(t,M(t’)) you have to pick t and t’. In the binary treatment case, this choice is obvious because there are only two values; t=1, t’= 0. When you have four category exposure, depending on your substantive question, you can pick different comparisons. 
> 
> Best,
> Kosuke
> 
> Department of Politics
> Princeton University
> http://imai.princeton.edu
> 
> On Nov 28, 2013, at 7:19 AM, Mashhood Sheikh <senor_massao at hotmail.com> wrote:
> 
>> Dear Dustin and Kosuke,
>> 
>> Is it possible to estimate the direct and indirect effects when there is an interaction between exposures (X1, X2), regressed on outcome Y? Since X2 is also a mediator-outcome confounder between the mediation model X1>M>Y, and X1 is also a mediator-outcome confounder between the mediation model X2>M>Y, it is important to include them in the model as covariates, but than what about the interaction between them? Note: all variables, X1, X2, M, and Y are binary.
>> 
>> I would highly appreciate any tips...
>> 
>> 
>> Thankfully,
>> Mashhood
>> Institute of Community Medicine,
>> University of Tromsø,
>> Norway.
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