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From |
Formoso Giulio <GFormoso@regione.emilia-romagna.it> |

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

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

Date |
Mon, 11 Feb 2013 12:19:49 +0000 |

Updating my previous post of about 2 hours ago (see below): analyzing data as Clyde proposed (vs introducing baseline values as covariate, as I did before), confidence intervals widen if, as unit of analysis, I use health districts (n=42), whereas become much narrower if I use physicians (N=about 4000). Giulio -----Messaggio originale----- Da: Formoso Giulio Inviato: lunedì 11 febbraio 2013 11:46 A: statalist@hsphsun2.harvard.edu Oggetto: R: st: baseline adjustment in linear mixed models 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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Clyde B Schechter Inviato: domenica 10 febbraio 2013 00:03 A: statalist@hsphsun2.harvard.edu 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).

**References**:**Re: st: baseline adjustment in linear mixed models***From:*Clyde B Schechter <clyde.schechter@einstein.yu.edu>

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