longitudinal with 2 time points
Thank you Ted for pointing this out. See my response to John's reply. What would you think of the model 5 where I used ANCOVA on the difference between week 5 & baseline and also included baseline as a covariate? Thanks John ----- Original Message ---- From: Charles E. (Ted) Wright <cewright at uci.edu> To: John Maindonald <john.maindonald at anu.edu.au> Cc: array chip <arrayprofile at yahoo.com>; r-sig-mixed-models at r-project.org Sent: Wed, August 11, 2010 5:34:21 AM Subject: Re: [R-sig-ME] longitudinal with 2 time points Keep in mind that running an ANOVA on the difference is not the same thing as using the baseline data as a covariate in an ANOVA on the Week 4 data. Essentially the ANOVA on the differences is like the ANCOVA with the slope constrained to be 1. Ted Wright
On Wed, 11 Aug 2010, John Maindonald wrote:
All these are possibilities, except maybe making baseline measurement a random factor.? This would make sense only if data divide into groups, and you want the baseline effect to vary randomly from group to group. That may limit your ability to estimate parameters that are of interest. In most circumstances that I am familiar with, it makes better sense to treat baseline effect as fixed. John. John Maindonald? ? ? ? ? ? email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473? ? fax? : +61 2(6125)5549 Centre for Mathematics & Its Applications, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200. http://www.maths.anu.edu.au/~johnm On 11/08/2010, at 8:11 AM, array chip wrote:
Hi, I am wondering if it is still meaningful to run a mixed model if a longitudinal dataset has only 2 time points (baseline and week 4)? Would it
be
more appropriate to simply take the difference between the 2 time points and run ANOVA (ANCOVA) on the difference? what about still running mixed model on the difference of the 2 time points, but adding baseline measurement as a random factor? Thanks for sharing your thoughts. John
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