# Generalized linear models (covariates and splitting files)

I am using generalized linear models with one response variable and 6 predictors (1 covariate and 5 factors). I want to assess the effect of smoking on my response variable. When I split my participants in sex groups and then in three smoking groups (smokers, ex-smokers, non-smokers) I find weaker associations than when I add smoking in the model (as a factor since it is a categorical variable) and split the participants in sex groups.

Could anyone please explain why is there this difference and what would be the best practice in this model?

Thank you!

ps: when I split in three smoking groups, the associations are similar per smoking group, they are just weaker.

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What is your sample size? It could be that that the sample size is not very large and now you have essentially created 6 groups. The effects may be real but different by gender. What are the 5 factors? Keep in mind that 3 genders with three smoking groups makes 3x2=6 groups. –  Michael Chernick Jul 23 '12 at 10:40
I have 32,484 (20,008 women and 14,619 men) people. But when I run the models, it says that only about 5,ooo are included (probably because of missing values in some covariates). My dependent variable is body mass index and the factors are alcohol drinking frequency, alcohol amount, economic status, index of deprivation, physical activity level. –  Vasia Jul 23 '12 at 10:45
The fact that most of your observations have missing data is worrisome. –  Peter Flom Jul 23 '12 at 11:21
Shouldn't 20,008 women + 14,619 men = 34,627 people, instead of 32,484? –  gung Jul 23 '12 at 13:49
It sounds like you tried your model 2 ways, entering smoking first or entering sex first, and found 2 different patterns of results. Is this the basis for your question? –  gung Jul 23 '12 at 14:02

If I am following you, you are doing one of two things: 1) Controlling for what you are trying to analyze. In the non-smoking group, there can be no association between smoking and BMI (or anything else). 2) Comparing the three groups (non-smokers, ex-smokers, current smokers) somehow, after separating them. I am not sure how you would then find associations: At most you could find different levels of BMI.

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My goal is to find whether there are associations between alcohol and BMI (body mass index). I have to control though for potential confounding factors (such as economic status, index of deprivation, physical activity level, age) and one of them is smoking. Apart from alcohol and the confounders that I always add, when I put smoking as a factor my associations are similar compared to when I do not put smoking. When I separate in smoking groups (and ran the generalized linear models with BMI as the dependent variable and all the rest as factors and covariates) I find much weaker associations. –  Vasia Jul 23 '12 at 12:38
So, I am getting very confused about what is the best practice. I hope that makes more sense. –  Vasia Jul 23 '12 at 12:39