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The following message was posted yesterday on SEMNET and is of interest to Mplus users. SEMNET readers may be interested in a new paper: Muthen, B. (2011). Applications of Causally Defined Direct and Indirect Effects in Mediation Analysis using SEM in Mplus. Submitted for publication. which is available at http://www.statmodel.com/examples/penn.shtml#extendSEM together with Mplus scripts. The paper discusses causallydefined effects for mediation analysis in an SEM context for continuous, binary, ordinal, nominal, and count variables. With noncontinuous variables, causallydefined effects have not been available in SEM software until now. The paper discusses maximumlikelihood and Bayesian analysis for nonnormal effects, latent response variable mediators, Monte Carlo simulations for planning new studies, sensitivity analysis when violating sequential ignorability, and generalizations to continuous and categorical latent variables. Furthermore, the paper is the first to present a method for mediation with a nominal mediator. One of the illustrations is an analysis of Judea Pearl's hypothetical example with binary mediator and binary outcome that was debated on SEMNET in September. 


Mplus users who do mediation analysis are urged to study the paper in the previous posting. The research in this paper elaborates and extends our previous recommendations of how to report indirect and direct effects. Some of the analyses performed in the new paper require Mplus Version 6.12 which is scheduled to be released next week. 


I have just read the new paper on Mediation and had a couple of basic questions. 1) How are the DE, TIE, PIE parameters interpreted for a count or negative binomial outcome? It looks as though parameters are exponentiated before being entered into these formulas from the example code in the table 512. 2) I am also new to the Causal Effect modeling approach, so I will go back and read some more of the introductory papers, but I wondered what the basic change to the formulas would be with say 3 or 4 treatments as opposed to two conditions. 


1) Count and negbin modeling focus on the log mean (often called the log rate), whereas the casual effect formulas consider the mean (that is, the rate). Hence the exponentiation. 2) You would do the analysis jointly for all treatments and control groups, so several dummy covariates. And then use the formulas for one treatment at a time. 


When examining mediation using parallel process growth models, several studies have looked at the relationship between X (e.g.tx condition) and subsequent M & Y parallel process models. However, I have not been able to locate any examples for which X & M are parallel process models and Y is a future outcome. Do you see any inherent methodological problems for a model in which the relationship between both initial status and growth of X and its relationship with the future outcome Y is thought to be mediated by the slope of M? If such a model is plausible, does the model presented below appear to represent an adequate means for examining such a relationship? MODEL: !Growth Model Xint Xslp  X1@0 X2@0.5 X3@1 X4@1.5 X5@2 X6@2.5; Mint Mslp  M1@0 M2@0.5 M3@1 M4@1.5 M5@2 M6@2.5; Xint with Mint; Mslp with Mint; Xint with Xslp; Xslp on Mint; Mslp on Xint (a1); Mslp on Xslp (a2); Y on Mslp (b); Y on Xint Xslp Mint; Model Constraint: new (med1 med2 ); med1=a1*b; med2=a2*b; 


Such modeling seems reasonable, generally speaking. The time ordering needs to be clear  it seems odd to have Xslp on Mint if M mediates X. 


Hi there, I'm brand new to MPlus so I apologize if this is extremely basic.I am attempting to decompose several significant interactions. I have one categorical predictor (sexual victimization history) and have found that my continuous moderators (life stress and social support) significantly moderate the relationship between my predictor and outcome variables (of which I have threeall are categorical). Can I do this in Mplus? 


Yes, you can do this. See Example 3.18. It is more complicated than your model but you should be able to use that as a starting point. 

Boliang Guo posted on Wednesday, March 19, 2014  2:43 am



2011 paper is really great!! especially for model with nominal mediator, thank. Here is also two more points in my mind for your kind attention, based on equation (16), could I say the PIE is mathematically equivalent to the traditional mediation estimate, e.g a*b? could the definition be generalized to case when x is continuous variable? i.e. X is not the treatment status but the dose of drug, so the effect of X is change in y due to unit change in x?thanks. 


I will send you a new paper that is forthcoming in the SEM journal where I lay out the counterfactual formulas for the continuous X case. 


If I wanted to use the model constraint command to estimate create a sensitivity plot of a treatment outcome error correlation rather than a treatment mediator correlation is it correctly understood that I can use the estimate of beta1 in "run 23" in your 2011 paper on causal mediation analysis? I.e. an example with a treatment an outcome and two independent variables: MODEL: outcome with treatment (cov) ; outcome (sig) ; treatment (sig2) ; outcome on x1 x2; treatment on x1 x2 ; MODEL CONSTRAINT: plot(beta1) ; loop(mod, 1,1,0.1) ; NEW(rhocurl rho) ; rhocurl=cov/(sqrt(sig)*sqrt(sig2)) ; rho=mod ; beta1=(sqrt(sig)/sqrt(sig2))*(rhocurlrho*sqrt((1rhocurl*rhocurl)/(1rho*rho))) ; plot: type=plot2 ; 


I haven't thought about that. It is a different setting. You would have to go through the steps of Section 10 to see if that is right. 


Hi Bengt thank you for your response. I have essentially just reproduced the script from "run24" where I use "beta1" which is the effect of the mediator on the outcome variable but do not multiply it with the effect of the treatment on the mediator to obtain the indirect effect but simply keep the "direct" effect. Would this not simply correspond to the "total" effect if it is not theorized to be a mediator but simply the total effect of an independent variable after the other independent variables are held constant, and thus obtain the sensitivity of the treatmentoutcome relationship of an error correlation between the two e.g. an unobserved confounder? 

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