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st: Mixed model for longitudinal data: Time discrete or continuous?


From   Abdelouahid Tajar <a_tajar@hotmail.co.uk>
To   statalist <statalist@hsphsun2.harvard.edu>
Subject   st: Mixed model for longitudinal data: Time discrete or continuous?
Date   Fri, 1 Jun 2012 10:52:39 +0100

Hi,



For mixed models (using xtmixed, xtlogit ect...) with
longitudinal data in the standard situation with two covariates: time (0,1,2,3)
(as an example) and a binary variable (0,1) for group.

People often use time as a CONTINUOUS variable.



In models which include an interaction term between time and group (which often
is the case) we have three fixed effect parameters b1=time, b2=group and
b3=interaction_time_group.



Now if time is treated as DISRETE
with time (0,1,2,3) we have 7 parameters: 1 for group,  3 for the 3
dummies of time and the 3 for interaction terms between time and group.



Compared to the model with discrete time, the model with time as continuous
(which could also have a time^2 term) has clearly fewer parameters even when
time^2 is included.



My question is how to choose between the two models? The continuous time model and
discrete time model? 





Many thanks in advance for any comments.



Abdelouahid


 		 	   		  
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