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re: re: Re: st: binary mediation command


From   "Ariel Linden. DrPH" <ariel.linden@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   re: re: Re: st: binary mediation command
Date   Sat, 25 Jun 2011 13:26:19 -0700

Kristian does an excellent job of adding some important points to the
discussion of mediation analysis. A couple of follow-up items:

VanderWeele's marginal structural modeling approach is not currently
available in Stata (Steve Samuels and I have been noodling this around for
about a year, and we will hopefully write such a program before we're put
out to pasture).

One other interesting program that is in Stata and worthy of note is
-gformula-. There is an accompanying paper (found with the example data
provided with the program: 
Daniel, R. M., De Stavola, B. L., and Cousens, S. N. 2010. gformula:
Estimating causal effects in the presence of time-varying confounding or
mediation using the g-computation formula. The Stata Journal (submitted))

This approach should address the very real issues that Kristian raised
earlier as threats to validity of mediation analysis and can handle them in
a somewhat different fashion than that of marginal structural models.

Ariel

Date: Fri, 24 Jun 2011 09:53:58 +0200
From: Kristian Karlson <kristian.karlson@gmail.com>
Subject: re: re: Re: st: binary mediation command

Let me add a bit to Ariel's suggestions. The method you choose will
often depend on the question--and data--at hand. The Imai et al.
approach is focused on the identification of causal indirect
effects/mediation, not "descriptive" or "associational" indirects
effects. The former is important if you have experimental and
quasi-experimental data, while the latter often is used as a summary
measure of concomitant variation in three or more variables.
Sociologist S.L. Morgan has an excellent article forthcoming in
Sociological Methods and Research, which discusses these issues (in a
sociological debate on the origins of class inequalities in
educational attainment).

The methods by Erikson et al., Karlson et al., and Fairlie are
"associational". They do not stipulate anything about causality (but
see Morgan's article).

The Imai et al. approach concerns "causal" indirect effects, and to
identify these effects they assume "sequential ignorability", which
means (1) that selection into treatment (independent variable) is
random conditional on pre-treatment covariates, and (2) that selection
into treatment is random conditional on treatment and pre-treatment
covariates. While the first assumption is credible when treatment is a
randomized experiment, the second assumption is restrictive and
untestable (although Imai et al. suggest some simulation techniques to
evaluate the robustness of results to this assumption). The weakness
of this second assumption is best explained by Sobel, in his 2008
article, which I consider one of the most important contributions to
this literature (http://jeb.sagepub.com/content/33/2/230.short). Sobel
also develops the conditions under which we can identify causal
indirect effects with randomized experiments (and these conditions are
very restrictive).

Other methods exist for the estimation of "causal" indirect effects:
VanderWeele has some developments within the marginal structural
models approach
(http://journals.lww.com/epidem/Abstract/2009/01000/Marginal_Structural_Mode
ls_for_the_Estimation_of.6.aspx).
This approach assumes that section is random conditional on
observables, which may be an overly restrictive assumption. I do not
know whether VanderWeele's approach is implemented in Stata (but as
far as I know there have been some writings on the implementation of
marginal structural models in Stata).

Best
Kristian


- -------------
From	  "Ariel Linden, DrPH" <ariel.linden@gmail.com>
To	  <statalist@hsphsun2.harvard.edu>
Subject	  re: Re: st: binary mediation command
Date	  Fri, 17 Jun 2011 09:32:47 -0700

There are several other approaches to mediation analysis. One technique that
I have recently become enamored with is a non-model based approach that can
accommodate any combination of data types for the mediator and outcome. This
eliminates the concerns that arise with forcing a non-linear variable into a
linear approach (using the standard Baron-Kenny model). This program by Imai
et al is in R, but they are about to come out with a Stata version (it's not
as complete as the R version but handles most jobs you'd ever need).

See: http://imai.princeton.edu/projects/mechanisms.html, in particular:

Imai, Kosuke, Luke Keele and Dustin Tingley (2010) A General Approach to
Causal
  Mediation Analysis, Psychological Methods 15(4) pp. 309-334.

Imai, Kosuke, Luke Keele and Teppei Yamamoto (2010) Identification,
Inference, and
  Sensitivity Analysis for Mediation Effects, Statistical Sciences, 25(1)
pp.  51-71.

I hope this helps

Ariel


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