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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: suest across two svy:glm models to test interaction |

Date |
Mon, 8 Sep 2008 23:36:22 +0100 (BST) |

--- Christy McKinney <Christy.McKinney@utsouthwestern.edu> wrote: > I am examining the association between binge drinking and > neighborhood poverty using survey data. Because binge drinking is > highly prevalent I would like to use glm to estimates relative risks > (instead of logistic regression & odds ratios). Why is high prevalence an argument against logistic regression? The only argument I have heard is that in those cases the odds ratios no longer closely resemble risk ratios, but that is a fallacy: Odds ratios are intended to measure the ratio of odds, and should be interpreted that way. The fact that they no longer approximate something they weren't suppose to approximate anyhow is not a valid argument. Odds ratios are easy to interpret once you stop trying to interpret them in metrics they were not made for, like risk differences or risk ratios. The odds, the expected number successes for every failure, is a measure for the likelihood of an event that is different, but equally easy, as the probability. The ratio of odds is a simple way of showing how much the likelihood of an even differs between groups. That is all you need when interpreting odds ratios, any reference to risk ratios will only complicate things. Do you have multiple observations in the same neighborhood? In that case observations are nested within neighborhood and you need to take this multilevel structure into account. > Past year binge drinking is categorized into 3 groups: no binge > (referent); binge <1 month; and binge >=1 month. In one glm model, I > compare binge <1 month (binge2lt) to no binge drinking. In a separate > glm model, I compare binge >=1 month (binge2gt) to no binge > drinking. A more appropriate model would take into account the ordinal nature of your data. Take a look at -help ologit- and -ssc describe gologit2-. If you have multilevel data you should probably use -gllamm-, see -ssc describe gllamm- and www.gllamm.org . > I want to evaluate whether the risk of binge drinking associated with > neighborhood poverty is different for men and women (e.g. does sex > modify the relation between binge drinking and neighborhood > poverty?). I am new to the suest command and am not sure I am using > properly. Can I use the suest command to combine the two separate > models and test the interaction jointly across binge drinking > categories? You can, but you shouldn't. Just add the interaction like you would in any other situation, create the interaction term (e.g. -gen povXfem = poverty*female -) and add the interaction term into your analysis. There is a big problem with this, as is discussed here: http://www.nd.edu/~rwilliam/oglm/index.html . However, I have not seen a truly convincing solution to it, most solutions are very smart but way too fragile for my taste. Hope this helps, Maarten ----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands visiting address: Buitenveldertselaan 3 (Metropolitan), room N515 +31 20 5986715 http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * 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: suest across two svy:glm models to test interaction***From:*"Christy McKinney" <Christy.McKinney@utsouthwestern.edu>

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