[Mediation-information] Question about results from R mediation package

[Teppei Yamamoto]

Hi Alex,

Kosuke is right about the scale of the output. That is, the effect 
represents the average difference in the time until death between the 
treatment and control conditions, decomposed into indirect and direct 
components.

Looking at your code, I think you are not correctly using the 
"covaritates" argument in the mediate function --- you don't have to 
write out those variable names unless you want to make inferences 
conditional on specific values of those covariates. This is sometimes 
called the "moderated mediation" analysis by psychologists. See the help 
file, especially the example section.

In your message, you say at the beginning that your treatment is 
continuous but then you ask a question about a categorical treatment 
variable. Regardless, you can specify a specific contrast using the 
"treat.value" and "control.value" arguments. Again please refer to the 
help file.

Best,
Teppei

Kosuke Imai wrote:
> Hi Alex,
>
>    The scale of the quantities of interest (the average causal mediation effects and average direct effects) is the same as that of the outcome variable; that is, the time to death in the survival analysis.  I'm ccing my collaborators through our mailing list so that they can confirm: see https://r-forge.r-project.org/mail/?group_id=1070
>
> Best,
> Kosuke
>
> Department of Politics
> Princeton University
> http://imai.princeton.edu
>
> On Apr 11, 2012, at 9:33 AM, Alexander Weiss wrote:
>
>> Dear Prof. Imai:
>>
>> I have recently been asked to determine whether the effect of cigarette smoking
>> (non = 0, former = 1, present = 2) mediates the effect of a personality trait
>> (Agreeableness, a continuous variable scaled as a z-score) on time to death in
>> an accelerated failure time model, i.e., a model of the type survreg(Surv(y))
>> with a Weibull distribution in R.
>>
>> I used the package developed by yourself and your colleagues, though was
>> a bit puzzled by the outcome as I do not understand the scale of the parameter
>> estimates. I specified the outcome model and mediator models as
>> follows:
>>
>> # AFT model for time to death
>> model.y<- survreg(Surv(weekstodeath_ssn_dob,mortality_status_dob)
>>              ~z_a + pat_sex + tribaseage + grade1 + diabetic +
>>                cvd_broa + selfhealth_1 + adlbase + iadlbase + smoking2
>>                + z_n + z_e + z_o + z_c,dist="weibull",data=ffi_mediator)
>>
>> # Create polr model for predicting smoking from Agreeableness
>> model.m<- (polr(data=ffi_mediator,Hess=T,method="probit",smoking2 ~ z_a +
>> pat_sex
>>                  + tribaseage + grade1 + diabetic + cvd_broa +
>>                  selfhealth_1 + adlbase + iadlbase + z_n + z_e + z_o + z_c))
>>
>> and used the following for the mediate() function:
>>
>> smoking_agreeableness_covariates<-
>>   mediate(model.m,model.y,sims=1000,treat="z_a",mediator="smoking2",
>>   covariates=list("pat_sex","tribaseage","grade1","diabetic1","cvd_broa",
>>   "selfhealth_1","adlbase","iadlbase","z_n","z_e","z_o","z_c"))
>>
>> The results of the analyses are as attached as a text file.
>>
>> My question concerns the parameter estimates obtained. First, I cannot quite
>> work out what metric they are on as they are not in the same as the AFT
>> analysis. Moreover, I am not wholly clear on how to interpret the results
>> given that they seem to be for cases in which the treatment variable is
>> categorical.
>>
>> I appreciate you are busy, but I would most appreciate your assistance in this
>> matter.
>>
>> Sincerely,
>>
>> Alex
>>
>>
>>
>> --
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>> <mediation_results_11_april_2012.txt>
>
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-- 
====================================
Teppei Yamamoto
Assistant Professor
Department of Political Science
Massachusetts Institute of Technology
http://web.mit.edu/teppei/www/
====================================