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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