# Summary of Mediation Analysis using Bayesian Regression Models

This vignettes demonstrates the `mediation()`-function. Before we start, we fit some models, including a mediation-object from the mediation-package and a structural equation modelling approach with the lavaan-package, both of which we use for comparison with brms and rstanarm.

## Mediation Analysis in brms and rstanarm

``````library(bayestestR)
library(mediation)
library(brms)
library(rstanarm)

data(jobs)

set.seed(123)
# linear models, for mediation analysis
b1 <- lm(job_seek ~ treat + econ_hard + sex + age, data = jobs)
b2 <- lm(depress2 ~ treat + job_seek + econ_hard + sex + age, data = jobs)

# mediation analysis, for comparison with brms
m1 <- mediate(b1, b2, sims = 1000, treat = "treat", mediator = "job_seek")``````
``````# Fit Bayesian mediation model in brms
f1 <- bf(job_seek ~ treat + econ_hard + sex + age)
f2 <- bf(depress2 ~ treat + job_seek + econ_hard + sex + age)
m2 <- brm(f1 + f2 + set_rescor(FALSE), data = jobs, cores = 4)``````
``````# Fit Bayesian mediation model in rstanarm
m3 <- stan_mvmer(
list(job_seek ~ treat + econ_hard + sex + age + (1 | occp),
depress2 ~ treat + job_seek + econ_hard + sex + age + (1 | occp)),
data = jobs,
cores = 4,
refresh = 0
)``````

`mediation()` is a summary function, especially for mediation analysis, i.e. for multivariate response models with casual mediation effects.

In the models `m2` and `m3`, `treat` is the treatment effect and `job_seek` is the mediator effect. For the brms model (`m2`), `f1` describes the mediator model and `f2` describes the outcome model. This is similar for the rstanarm model.

`mediation()` returns a data frame with information on the direct effect (median value of posterior samples from treatment of the outcome model), mediator effect (median value of posterior samples from mediator of the outcome model), indirect effect (median value of the multiplication of the posterior samples from mediator of the outcome model and the posterior samples from treatment of the mediation model) and the total effect (median value of sums of posterior samples used for the direct and indirect effect). The proportion mediated is the indirect effect divided by the total effect.

The simplest call just needs the model-object.

``````# for brms
mediation(m2)
#> # Causal Mediation Analysis for Stan Model
#>
#>   Treatment: treat
#>   Mediator : job_seek
#>   Response : depress2
#>
#> Effect                 | Estimate |          89% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE)    |   -0.040 | [-0.110,  0.031]
#> Indirect Effect (ACME) |   -0.015 | [-0.036,  0.004]
#> Mediator Effect        |   -0.240 | [-0.285, -0.195]
#> Total Effect           |   -0.055 | [-0.129,  0.018]
#>
#> Proportion mediated: 28.14% [-71.11%, 127.40%]

# for rstanarm
mediation(m3)
#> # Causal Mediation Analysis for Stan Model
#>
#>   Treatment: treat
#>   Mediator : job_seek
#>   Response : depress2
#>
#> Effect                 | Estimate |          89% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE)    |   -0.040 | [-0.111,  0.031]
#> Indirect Effect (ACME) |   -0.018 | [-0.037,  0.002]
#> Mediator Effect        |   -0.241 | [-0.286, -0.197]
#> Total Effect           |   -0.057 | [-0.130,  0.017]
#>
#> Proportion mediated: 30.59% [-75.65%, 136.82%]``````

Typically, `mediation()` finds the treatment and mediator variables automatically. If this does not work, use the `treatment` and `mediator` arguments to specify the related variable names. For all values, the 89% credible intervals are calculated by default. Use `ci` to calculate a different interval.

## Comparison to the mediation package

Here is a comparison with the mediation package. Note that the `summary()`-output of the mediation package shows the indirect effect first, followed by the direct effect.

``````summary(m1)
#>
#> Causal Mediation Analysis
#>
#> Quasi-Bayesian Confidence Intervals
#>
#>                Estimate 95% CI Lower 95% CI Upper p-value
#> ACME            -0.0157      -0.0387         0.01    0.19
#> ADE             -0.0438      -0.1315         0.04    0.35
#> Total Effect    -0.0595      -0.1530         0.02    0.21
#> Prop. Mediated   0.2137      -2.0277         2.70    0.32
#>
#> Sample Size Used: 899
#>
#>
#> Simulations: 1000

mediation(m2, ci = .95)
#> # Causal Mediation Analysis for Stan Model
#>
#>   Treatment: treat
#>   Mediator : job_seek
#>   Response : depress2
#>
#> Effect                 | Estimate |          95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE)    |   -0.040 | [-0.124,  0.046]
#> Indirect Effect (ACME) |   -0.015 | [-0.041,  0.008]
#> Mediator Effect        |   -0.240 | [-0.294, -0.185]
#> Total Effect           |   -0.055 | [-0.145,  0.034]
#>
#> Proportion mediated: 28.14% [-181.46%, 237.75%]

mediation(m3, ci = .95)
#> # Causal Mediation Analysis for Stan Model
#>
#>   Treatment: treat
#>   Mediator : job_seek
#>   Response : depress2
#>
#> Effect                 | Estimate |          95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE)    |   -0.040 | [-0.129,  0.048]
#> Indirect Effect (ACME) |   -0.018 | [-0.042,  0.006]
#> Mediator Effect        |   -0.241 | [-0.296, -0.187]
#> Total Effect           |   -0.057 | [-0.151,  0.033]
#>
#> Proportion mediated: 30.59% [-221.09%, 282.26%]``````

If you want to calculate mean instead of median values from the posterior samples, use the `centrality`-argument. Furthermore, there is a `print()`-method, which allows to print more digits.

``````m <- mediation(m2, centrality = "mean", ci = .95)
print(m, digits = 4)
#> # Causal Mediation Analysis for Stan Model
#>
#>   Treatment: treat
#>   Mediator : job_seek
#>   Response : depress2
#>
#> Effect                 | Estimate |            95% ETI
#> ------------------------------------------------------
#> Direct Effect (ADE)    |  -0.0395 | [-0.1237,  0.0456]
#> Indirect Effect (ACME) |  -0.0158 | [-0.0405,  0.0083]
#> Mediator Effect        |  -0.2401 | [-0.2944, -0.1846]
#> Total Effect           |  -0.0553 | [-0.1454,  0.0341]
#>
#> Proportion mediated: 28.60% [-181.01%, 238.20%]``````

As you can see, the results are similar to what the mediation package produces for non-Bayesian models.

## Comparison to SEM from the lavaan package

Finally, we also compare the results to a SEM model, using lavaan. This example should demonstrate how to “translate” the same model in different packages or modeling approached.

``````library(lavaan)
data(jobs)
set.seed(1234)

model <- ' # direct effects
depress2 ~ c1*treat + c2*econ_hard + c3*sex + c4*age + b*job_seek

# mediation
job_seek ~ a1*treat + a2*econ_hard + a3*sex + a4*age

# indirect effects (a*b)
indirect_treat := a1*b
indirect_econ_hard := a2*b
indirect_sex := a3*b
indirect_age := a4*b

# total effects
total_treat := c1 + (a1*b)
total_econ_hard := c2 + (a2*b)
total_sex := c3 + (a3*b)
total_age := c4 + (a4*b)
'
m4 <- sem(model, data = jobs)
summary(m4)
#> lavaan 0.6-6 ended normally after 25 iterations
#>
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of free parameters                         11
#>
#>   Number of observations                           899
#>
#> Model Test User Model:
#>
#>   Test statistic                                 0.000
#>   Degrees of freedom                                 0
#>
#> Parameter Estimates:
#>
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#>
#> Regressions:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   depress2 ~
#>     treat     (c1)   -0.040    0.043   -0.929    0.353
#>     econ_hard (c2)    0.149    0.021    7.156    0.000
#>     sex       (c3)    0.107    0.041    2.604    0.009
#>     age       (c4)    0.001    0.002    0.332    0.740
#>     job_seek   (b)   -0.240    0.028   -8.524    0.000
#>   job_seek ~
#>     treat     (a1)    0.066    0.051    1.278    0.201
#>     econ_hard (a2)    0.053    0.025    2.167    0.030
#>     sex       (a3)   -0.008    0.049   -0.157    0.875
#>     age       (a4)    0.005    0.002    1.983    0.047
#>
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>    .depress2          0.373    0.018   21.201    0.000
#>    .job_seek          0.524    0.025   21.201    0.000
#>
#> Defined Parameters:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>     indirect_treat   -0.016    0.012   -1.264    0.206
#>     indirct_cn_hrd   -0.013    0.006   -2.100    0.036
#>     indirect_sex      0.002    0.012    0.157    0.875
#>     indirect_age     -0.001    0.001   -1.932    0.053
#>     total_treat      -0.056    0.045   -1.244    0.214
#>     total_econ_hrd    0.136    0.022    6.309    0.000
#>     total_sex         0.109    0.043    2.548    0.011
#>     total_age        -0.000    0.002   -0.223    0.824

# just to have the numbers right at hand and you don't need to scroll up
mediation(m2, ci = .95)
#> # Causal Mediation Analysis for Stan Model
#>
#>   Treatment: treat
#>   Mediator : job_seek
#>   Response : depress2
#>
#> Effect                 | Estimate |          95% ETI
#> ----------------------------------------------------
#> Direct Effect (ADE)    |   -0.040 | [-0.124,  0.046]
#> Indirect Effect (ACME) |   -0.015 | [-0.041,  0.008]
#> Mediator Effect        |   -0.240 | [-0.294, -0.185]
#> Total Effect           |   -0.055 | [-0.145,  0.034]
#>
#> Proportion mediated: 28.14% [-181.46%, 237.75%]``````

The summary output from lavaan is longer, but we can find the related numbers quite easily:

• the direct effect of treatment is `treat (c1)`, which is `-0.040`
• the indirect effect of treatment is `indirect_treat`, which is `-0.016`
• the mediator effect of job_seek is `job_seek (b)`, which is `-0.240`
• the total effect is `total_treat`, which is `-0.056`