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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: Re: st: Re: new to xtmixed - basic question |

Date |
Fri, 22 Jul 2011 07:36:41 -0700 |

Ricardo, First, let me correct a mistake in my previous post. Instead of i.treatment##c.time##c.time2 it should have been i.treatment##c.time i.treatment##c.time2 (an interaction between time and time2 is not needed unless you want to introduce a cubic time term.) After that, before just looking at the terms individually I would do an omnibus test of all terms that include treatment: -test 2.treatment 3.treatment 2.treatment#time 3.treatment#time 2.treatment#time2 3 treatment#time2- If that test _is_ significant, then you can separately look at the effect of treatment on the constant terms, linear terms and quadratic terms. It can happen that none of those is individually significant even when the omnibus test was--that is often difficult to explain. It means that overall treatment affects the trajectories as a whole although the particular effects on the constant, linear and quadratic terms are not quantified sufficiently precisely by the data to localize the effect or that in the treated groups there are different linear relationships among the linear and quadratic terms although the overall marginal values of those terms are not much changed. If the omnibus test is not significant, then your data do not provide evidence of any overall impact of treatment on the trajectory of met over time (or at least no impact that can be captured by a quadratic model). When you run a model incorporating a treatment term but no treatment X time interactions, you are fitting your data to a model in which the time course of met is constrained to look the same in all three treatment groups, except that there may be a vertical displacement between them. Otherwise put, the effect of treatment is to give a "boost" to met levels that persists unchanged over time. If that describes what theory says the treatments should do, then that is the model whose results you should rely on. The model with interaction terms is more general: it allows treatment to affect the way in which met varies over time. But if based on the science in your situation that is not what is expected, the constrained model without interaction terms may be more powerful for detecting a persistent boost effect. Clyde Schechter Department of Family & Social Medicine Albert Einstein College of Medicine Bronx, NY, USA -------------Original Message------------------------------- Thank you Clyde, In my model I did have -treatment- as a categorical variable, I just inadvertently dropped the "i." when I posted it to Statalist. When I use - i.treatment##c.time##c.time2- in the model all interaction terms are not significant and neither is the treatment effect. When I do not interact treatment and time, treatment is significant. * * 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/

**Follow-Ups**:**Re: Re: st: Re: new to xtmixed - basic question***From:*Ricardo Ovaldia <ovaldia@yahoo.com>

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