Hi.
My name is Yiu-Fai Yung.
Today, I'm going to introduce the CAUSALMED procedure
for causal mediation analysis.
In a causal mediation analysis, there
are three main types of variables, the treatment
variable, an outcome variable, and the mediator variable.
The treatment variable is considered
to be the root cause of the outcome.
And part of its causal effect is mediated through the mediator
variable.
Here are some examples.
A particular gene causally affects the chance
of getting lung cancer, and some of its effect
is mediated through its effect on increasing cigarette
smoking.
Or your daily stress leads to a sleep disorder,
and it is mediated through its induction
of emotional discomfort.
Or a youth program reduces delinquent behavior
in teenagers through its enhancement of teenagers'
moral and social values.
Common to all these examples, a hypothesized mechanism
or mediation process is represented
by the mediator variable.
The treatment effect through the mediation process
is called the mediated or indirect effect.
In the last example, the enhancement
of moral and social values of teenagers
describes a mediation process.
Notice that a direct arrow from program to delinquencies
remains here.
This represents the direct effect
of the treatment on the outcome.
But more accurately, this is the part of the treatment effect
that is not due to the specified mediation process.
Finally, the total treatment effect
includes both the mediated effect and the direct effect.
In a causal mediation analysis, you
study the causal mechanisms, and the mediation process
serves as an explanation of the treatment effects.
Specifically, you want to ask three main questions.
How much of a mediation effect there is?
How important the mediation process is?
And what the treatment effect would
be if you could somehow intervene in the mediation
process.
In practical research, especially in observational
studies, you cannot randomly assign subjects to different
treatment conditions.
For example, teenagers participate in the youth
program by their own choice.
Even if you can randomly assign subjects
to treatment conditions, random assignment
to mediated conditions is usually not plausible.
Because of the lack of random assignments,
some background pretreatment characteristics of the subjects
might have confounded the observed effects.
These background pretreatment characteristics
are also called confounding covariates
because they induce extraneous associations
among the treatment, mediator, and outcome variables
by acting as common causes as represented by the red arrows
in the causal diagram.
Therefore, causal mediation analysis
must also deal with these confounding associations
in order to get unbiased estimation
of the causal treatment and mediation effects.
PROC CAUSALMED, which was first available in SAS/STAT 14.3,
is designed to estimate causal mediation effects
in observational studies.
Theoretically, it is based on the counterfactual framework,
which defines causal mediation effects
by counterfactual outcomes.
Under the counterfactual outcome framework
and some statistical assumptions,
PROC CAUSALMED uses regression adjustment methods
to deal with confounding covariates
so that you can get unbiased estimation of causal mediation
effects.
Let me now demonstrate how you can
use the CAUSALMED procedure to estimate direct
and indirect causal effects.
In the youth program example, teenagers
enter the youth program voluntarily.
After one year, their moral and social values
were measured by a psychological scale.
Their delinquent behavior within two years after the program
began was recorded.
In addition to many variables in the causal mediation analysis,
several individual characteristics
that might serve as confounding covariates were also measured.
These include initial delinquency
of the subjects, their gender, their grade point
average for measuring academic performance, whether they were
introverted or extroverted, their socioeconomic status,
either high, medium, or low, their family support
on a four-point scale, and their initial moral and social
values.
These are all important confounding covariates
that need to be controlled for in the causal mediation
analysis.
Let me explain the CAUSALMED setup step-by-step here.
First, in the proc causalmed statement,
I input a simulated data set, Youth, with 800 observations.
In the COVAR statement, you list all the potential
confounding covariate effects.
In the MODEL statement, you specify
that the outcome variable, Delinquency,
is regressed on the treatment variable, Program,
and the mediator variable, Value.
The interaction effect is also included.
Notice that the actual modeling of the outcome
will also include those covariate effects that
are specified in the COVAR statement.
In the MEDIATOR statement, you specify
that the mediator value is regressed on the treatment
variable, Program.
Again, the actual modeling of the mediator
will also include those covariant effects
that are specified in the COVAR statement.
In the CLASS statement, you tell the procedure
which variables are categorical or class variables.
Variables that are not specified in the CLASS statement
are treated as continuous variables.
You also use the DESCENDING option for the Program variable
here because you want to use reverse alphabetical order
to indicate that a Yes value represents the treatment
condition.
As you can see from the first set of the output,
the Program variable uses reverse alphabetical order
for the levels.
PROC CAUSALMED assumes that the first ordered level,
here with the Yes value, represents the treatment
condition.
In the model information table, because both the Outcome
and Mediator variables are continuous,
identity lengths are used.
This means that linear regression models
are used for both the Outcome and Mediator variables here.
In the treatment profile table, you
can see that there were 243 subjects in the treatment
condition and 557 subjects in the control condition.
This is the main table of the mediation analysis results.
The total causal treatment effect
is minus 0.56, which is statistically significant.
On average, there is a reduction of 0.56 in delinquent behavior
after controlling for all confounding effects.
The natural indirect effect shows
that minus 0.11 of the total effect
is due to the mediation of the moral and social values
of the teenagers.
The remaining effect of minus 0.45
is the natural direct effect.
Because the natural direct effect
and natural indirect effect add up to the total effect,
dividing the entire effect by the total effect
and then multiplying by 100% will give you the percentage
due to mediation.
This percentage is 20% here.
So 20% of the total program effect
on reducing delinquent behavior is
due to the enhancement of moral and social values.
Today, I have demonstrated some main features of the CAUSALMED
procedure.
You can do much more with this procedure.
Let me summarize the features here.
First, the procedure can do maximum likelihood estimation
of various causal mediation and related effects
and their percentages by regression adjustment methods.
You can use either bootstrap or asymptotic formulas
to estimate standard errors and confidence intervals.
The procedure also supports various data types,
including continuous and binary outcome, treatment,
and mediator variables, and continuous and categorical
covariates.
A limited set of generalized linear models
is supported to fit the outcome and mediator models.
The procedures provide several two, three,
and four-way decompositions of total effects.
It also provides very flexible evaluation
of controlled direct effects and conditional mediation effects.
Finally, it supports analysis that
use the case-control design.
For more numerical demonstrations,
please see my SAS Global Forum paper.
For more information and additional resources,
please visit our support page.
Thank you.