
Hi,
 
I am trying a linear regression model where the dependent variable is the size of the heart corrected for the patient's height and weight. This is labelled as LAVI. The independent variables are race (european or non-eurpoean), age, sex (male or female) of the patient and whether they have diabetes and high blood pressure. sample size 2000 patients selected from a community.
 
when I model
model1<-lm(lavi~age+sex+race+diabetes+hypertension, data=tb1)
 and 
model2<-lm(lavi~age+sex+age*race+diabetes+hypertension, data=tb1)
 
in the first model race comes out as a significant predictor (p<0.005) where as in the second model race is not a significant predictor of lavi (p=.076)
 
in my dataset mean age is 55.2 years in the non-europeans and 56.7 years in the europeans (p <0.0001 by t.test). 
 
should I or should I not include the interaction (age*race) in the model. Is it an acceptable rule to put in interactions if there is a significant relation between the indepenedent variables in univariate analyses.
 
Many thanks
 
bragadeesh
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