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From |
"Maarten Buis" <M.Buis@fsw.vu.nl> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: can gllamm fit this? |

Date |
Tue, 11 Oct 2005 10:12:11 +0200 |

I had the same idea as Svend, though coming from a different discipline. In my parlance I would say that the model you are trying to estimate is recursive, so there is no need to simultaneously estimate equation 1 and 2. Consequently you can just estimate two separate logistic regressions as Svend suggested, and there is no need to use GLLAMM. Hope this helps, Maarten -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Svend Juul Sent: maandag 10 oktober 2005 23:23 To: statalist@hsphsun2.harvard.edu Subject: Re: st: can gllamm fit this? Bill wrote: I have three binary variables, say x1, x2, and x3. I want to fit two logistic regression models simultaneously, x2=b12*x1 and x3=b13*x1+b23*x2. I want to fit them simultaneously in order to calculate the indirect effect proportion = (indirect effect)/(total effect) = (b12*b23)/(b12*b23 + b13). Because the data are not continuous, I cannot use pathreg. I believe this model falls in the category of latent variable (SEM) using manifest variables, which I've read gllamm can fit. Any advice or guidance is appreciated, specifically how to specify the B matrix, or if I even need a B matrix. The documentation is pretty tough to work through. ----------------------------------- This isn't an answer, but a speculation from an epidemiologist who is used to think: "What is the question (or hypothesis)?" Bill's two equations can be put graphically: x1 ---------------> | x3 ------> x2 ------> It looks like what we epidemiologists call the confounding triangle (the untriangular look is only due to a practical shortcoming of text mode). However, x2 should not be considered a confounder since it may be in the causal pathway from x1 to x3. The corresponding questions are: 1. What is the overall (crude) effect of x1 on x3? 2. How much is explained by x2 being a consequence of x1 and a cause of x3? Example: Does smoking (x1) affect birthweight (x3)? Does smoking (x1) affect duration of pregnancy (x2)? Does duration of pregnancy (x2) affect birthweight (x3)? The crude x1-x3 association might reflect the x1 -> x2 -> x3 effects only, but there might also be a direct x1 -> x3 effect. The primary tool is -cc- (see [ST] cc). It gives the crude (x1 -> x3) odds ratio estimate and the adjusted x1 -> x3 estimate, i.e. the odds ratio estimate remaining when the x1 -> x2 -> x3 effect has been accounted for. (Actually, it seems that smoking increases the risk of preterm birth, but that it has an effect on birthweight beyond that). With -cc- you would: . cc x3 x1 . xx x3 x1 , by(x2) With -logistic- you would: . logistic x3 x1 . logistic x3 x1 x2 I don't know if this is useful to you. But I have the feeling that we are trying to invent the same wheel in various disciplines. Svend * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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