st: RE: Interpreting interactions

["Fogel, Ariel" <AFogel@urban.org>]

Hi Amal,

I predominantly lurk on Statalist, but am interested hearing an answer to this question if someone wiser than me feels compelled to answer it.

Unfortunately, I don't think you've provided enough information for anyone to know exactly what you're referring to since you have not introduced your project at all, are not using publicly available data (or any -sysuse- datasets), you've only provided snippets of your code, and you have not provided the exact output. I may be mistaken, but these all strike me as important (or helpful) details to include when trying to interpret results.

It may be useful next time (or if you don't hear a response for a few days) to reformat your email and try to better follow the advice laid out both in Statalist FAQ  <http://www.stata.com/support/faqs/resources/statalist-faq/> or in a very clearly written Statalist post by Nick Cox (Thanks Nick!) <http://www.stata.com/statalist/archive/2012-10/msg00174.html>.

All the best,

Ariel Fogel


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Amal Khanolkar
Sent: Wednesday, October 17, 2012 2:17 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: Interpreting interactions

Hi,

I ran the following set of logistic regression models:

1. Crude:

xi: logit vent6h i.mom_race2 sexx if age_mom!=. & parity!=. & gestcalc!=. & cigs_befx!=. & gestdb!=. & gesthy!=. & MBMI!=. & ht_cm!=. & plural==1 & edu_mom!=. & marriedx!=., or

2.  Adjusted for confounders:

xi: logit vent6h i.mom_race2 i.edu_mom i.marriedx age_mom sexx age_mom i.parity gestcalc i.cigs_befx i.gestdb i.gesthy i.MBMI ht_cm if plural==1., or

3. With interactions:

xi: logit vent6h i.mom_race2*i.edu_mom i.mom_race2*i.marriedx sexx age_mom i.parity gestcalc i.cigs_befx i.gestdb i.gesthy i.MBMI ht_cm if plural==1., or


I see that the Odds ratios (for racial groups) do not really change between models 1 and 2 - i.e. additional adjustment for potential confounders do not seen to affect the odds of a particular ethnic group of being diagnosed with my outcome of interest.

However, I see that the OR for ethnic group 2 go from 1.23 (95% CI 1.00 to 1.48) in model 1 to 1.13 (0.92 to 1.37) in model 2 to 2.33 (1.46 to 3.72) in model 3.

Model 3 only has the interactions in it, otherwise it is the same as model 2. How does one interpret the OR's for i.mom_race2 in model 3? I get the usual set of OR's for mom_race2 in the beginning of the output, and the the interactions towards the bottom of the model.

I assumed that the OR's in model 3 should be same as those in model 2 as I'm not additionally adjusting for anything new (ie it is the same as model 2 except for the interaction term).


Thanks,



Amal Khanolkar

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