[Mediation-information] Counterintuitive results

[Angel Rodriguez]

Dear Subscribers,

I've carried out the following model:

> library("sandwich")
> set.seed(2014)
> med.fit <- glm(estuprimas ~ edad_c + sexo + regalf + deprinf, family="binomial" ,data=child65)
> out.fit <- glm(benvii ~ edad_c + sexo + regalf + deprinf + estuprimas, family="binomial" ,data=child65)
> med.out <- mediate(med.fit, out.fit, treat = "deprinf", mediator = "estuprimas", robustSE = TRUE, sims=1000, control.value = "no", treat.value = "s\xed")
> summary(med.out)

Causal Mediation Analysis 

Quasi-Bayesian Confidence Intervals

                          Estimate 95% CI Lower 95% CI Upper p-value
ACME (control)           -0.012543    -0.029801     0.000732    0.07
ACME (treated)           -0.011498    -0.027808     0.000723    0.07
ADE (control)            -0.049137    -0.125894     0.031768    0.27
ADE (treated)            -0.048092    -0.121987     0.031410    0.27
Total Effect             -0.060635    -0.137208     0.021339    0.15
Prop. Mediated (control)  0.169334    -0.848191     1.686179    0.21
Prop. Mediated (treated)  0.147997    -0.854962     1.689611    0.21
ACME (average)           -0.012021    -0.028888     0.000728    0.07
ADE (average)            -0.048615    -0.124610     0.031589    0.27
Prop. Mediated (average)  0.158665    -0.851577     1.687895    0.21

Sample Size Used: 657 

Simulations: 1000 


If I understand well, this means that, of the total association of having experienced infant deprivation vs not having experienced it (variable deprinf) on having aged well (-0.06 (OR=0.94; CI95%= 0.87-1.02)) , 15.9% is via the mediator having primary education or more vs less than primary education (estuprimas). This does not make sense to me, because having primary education or more is a protective factor for aging well, as you can see in this model:

Call:
glm(formula = benvii ~ edad_c + sexo + regalf + deprinf + estuprimas, 
    family = "binomial", data = child65)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6054  -0.8433  -0.5597   1.0327   2.6291  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  7.29876    1.20323   6.066 1.31e-09 ***
edad_c      -0.10262    0.01619  -6.339 2.31e-10 ***
sexomujer   -0.89359    0.18639  -4.794 1.63e-06 ***
regalfMedia -0.63054    0.27055  -2.331  0.01977 *  
regalfBaja  -0.71116    0.23645  -3.008  0.00263 ** 
deprinfsí   -0.29268    0.23603  -1.240  0.21497    
estuprimas   0.33773    0.18910   1.786  0.07411 .  


I'd appreciate some insight into this result.

Thank you very much.

Angel Rodríguez-Laso
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