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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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/