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From |
"Kieran McCaul" <Kieran.McCaul@uwa.edu.au> |

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

Subject |
st: RE: question for listserve |

Date |
Sat, 13 Jun 2009 06:10:19 +0800 |

If, as you say, X3 is associated with both z and y, then X3 is potentially confounding the relationship between z and y. If this is the case, when X3 is added to the model, the odds ratio for z will change. So I think what you are asking is how to test this confounding effect. The change in the odds ratio for z in model 1 compared to the odds ratio for z in model2. Is that right? If so, you don't test this. It is a confounding effect, a bias. What you need to determine is whether or not the change is important. If the odds ratio for z in model 1 was 2.00 and in model 2 it was 1.98, I would say that X3 is not exerting much of a confounding effect (assuming z is a dichotomous variable). If, however, the odds ratio for z in model 2 was 1.50, I would have evidence that X3 was an important confounder because it has resulted in, what I consider to be, a large change in the odds ratio for z. Note, this does not apply if X3 is in the causal pathway between z and y. In this case, X3 is not a confounder but an intermediate variable and adjusting for X3 would bias your estimate of the effect of z. ______________________________________________ Kieran McCaul MPH PhD WA Centre for Health & Ageing (M573) University of Western Australia Level 6, Ainslie House 48 Murray St Perth 6000 Phone: (08) 9224-2701 Fax: (08) 9224 8009 email: Kieran.McCaul@uwa.edu.au http://myprofile.cos.com/mccaul http://www.researcherid.com/rid/B-8751-2008 ______________________________________________ If you live to be one hundred, you've got it made. Very few people die past that age – George Burns -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of scho0600@umn.edu Sent: Friday, 12 June 2009 8:44 PM To: statalist@hsphsun2.harvard.edu Subject: st: question for listserve I am trying to determine the statistical significance of the change in a parameter estimate for a predictor variable in a logistic regression when another predictor is added In other words, I have two models; model 1: logit y on z x1 x2 model 2: logit y on z x1 x2 x3. I want to check if the association between y and z is mediated in part by x3. I have already determined that z is associated with x3 and that y is associated with x3. I have tried to do this with the suest command but I do not trust the results since the two regressions are not run on independent samples. I would be grateful for any guidance from anyone on how to perform a valid statistical test the change in the parameter estimate for z when x3 is added to the regression. John T. Schousboe University of Minnesota * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: question for listserve***From:*scho0600@umn.edu

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