Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

R: st: baseline adjustment in linear mixed models


From   Formoso Giulio <[email protected]>
To   "[email protected]" <[email protected]>
Subject   R: st: baseline adjustment in linear mixed models
Date   Mon, 11 Feb 2013 10:45:44 +0000

Thank you Clyde for your kindness and clarity. If I compare intervention and control areas, their baseline values look much closer than their post-intervention values (curves clearly divaricate when the intervention starts). I don't know if, under these circumstances, baseline (pre-intervention) values  could be considered as distinctively influential as you say (if you have time, I'd like your opinion on this point). Actually I tried your alternative analysis: intervention vs control difference does not substantially change, although confidence intervals are wider.  

Thank you again! Giulio

-----Messaggio originale-----
Da: [email protected] [mailto:[email protected]] Per conto di Clyde B Schechter
Inviato: domenica 10 febbraio 2013 00:03
A: [email protected]
Oggetto: Re: st: baseline adjustment in linear mixed models

Giulio Formoso raises a question that comes up from time to time on Statalist: he plans to do a linear mixed model analysis of repeated-observations on a sample of units of observation, and asks if it is appropriate to include the baseline outcome value as a covariate.

Back to basics.  Let's think about a very simple statistical model that could be analyzed with the command:

-xtmixed y || participant: -

with no independent variables.  And let's assume that there are 2 observations for each participant.  In equation form, this model is:

y_ij = mu + u_i + eps_ij, where i indexes participants, j = 1,2 indexes observations.  The standard assumptions are the u_i ~ N(0, sig_u), eps_ij ~ N(0, sig_e), iid.  From this, we can deduce that y_i1 and y_i2 have a joint bivariate normal distribution with mean mu and variance V = sig_u^2 + sig_e^2, and correlation r = sig_u^2/(sig_u^2 + sig_e^2).



© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index