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From |
Clyde B Schechter <clyde.schechter@einstein.yu.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: baseline adjustment in linear mixed models |

Date |
Sat, 9 Feb 2013 23:03:23 +0000 |

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).

**Follow-Ups**:**R: st: baseline adjustment in linear mixed models***From:*Formoso Giulio <GFormoso@regione.emilia-romagna.it>

**R: st: baseline adjustment in linear mixed models***From:*Formoso Giulio <GFormoso@regione.emilia-romagna.it>

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