..
There is no longer a single sex effect or single age effect.
The age coefficient is the effect of age when sex==0 and age+age*sex is
the effect when sex==1. If the interaction term is not significant, it
is saying that the difference in age effects for males and females is no
greater than you might expect by chance alone.
The sex coefficient is the effect of sex when age=0, which may not be
all that useful. You should probably centre age before putting it into
the model:
qui summ age, meanonly
gen age_new = age - r(mean)
______________________________________________
Kieran McCaul MPH PhD
WA Centre for Health & Ageing (M573)
University of Western Australia
Level 6, Ainslie House
48 Murray St
Perth 6000
Phone: (08) 9224-2701
Fax: (08) 9224 8009
email: [email protected]
http://myprofile.cos.com/mccaul
http://www.researcherid.com/rid/B-8751-2008
______________________________________________
Epidemiology is so beautiful and provides such an important perspective
on human life and death,
but an incredible amount of rubbish is published. Richard Peto (2007)
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of moleps islon
Sent: Monday, 11 May 2009 8:20 AM
To: [email protected]
Subject: st: interpretation of disappearing siginifcance after adding
interaction term
How do I interpret the finding of disapperance of significance after
adding an interaction term ??
logistic infection sex age
...higly significant sex-beta while age is non significant...
gen sex_age=sex*age
logistic infection sex age sex_age
..no sigificant betas...
predxcon infeksjon,xvar(alder) class(sex_en) from (20) to (90) inc(5)
graph
.. showing what seems to be a significant splaying of the curves...
Any idea??
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