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


From   Ricardo Ovaldia <ovaldia@yahoo.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: Re: st: Re: new to xtmixed - basic question
Date   Fri, 22 Jul 2011 08:58:36 -0700 (PDT)

Thank you very much.
That was very helpful.

Ricardo

Ricardo Ovaldia, MS
Statistician 
Oklahoma City, OK


--- On Fri, 7/22/11, Clyde Schechter <clyde.schechter@einstein.yu.edu> wrote:

> From: Clyde Schechter <clyde.schechter@einstein.yu.edu>
> Subject: Re: Re: st: Re: new to xtmixed - basic question
> To: statalist@hsphsun2.harvard.edu
> Date: Friday, July 22, 2011, 9:36 AM
> 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.
> 
> 
> 
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