I suppose if you want to find out if the coefficient of age is the same
in each age group, you could fit an interaction between age and ageg.
xi: xtpois injury i.ageg*age, i(id)
this would give you the coefficient with respect to age in age group 1
(lincom age), in age group 2 (lincom age + IageXage_2), and in age group
3 (lincom age + IageXage_3).
So you could visually assess the differences or use testparm IageX* to
tell you if the interaction terms were statistically significant.
______________________________________________
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) 9224-2140
Fax: (08) 9224 8009
email: [email protected]
http://myprofile.cos.com/mccaul
_______________________________________________
The fact that no one understands you doesn't make you an artist.
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of
[email protected]
Sent: Friday, 5 September 2008 9:08 AM
To: [email protected]
Subject: st: Testing for Trend Linearity
I ran (in stata 9)
xi: xtpois injury age, i(id)
xi: xtpois injury age i.ageg, i(id)
where "injury" is a binary variable, "age" is continuous and "ageg" is
"age" as a categorical variable. All the p-values (age, _ Iageg_2, and
_Iageg_3) were >0.05.
Does this imply the trend does not deviate from linearity (at the 0.05
level)?
If not, does anyone have a suggestion(s) for a trend test that tests
for linearity?
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