Re: st: specifying random effects in -xtmixed- for pretest/posttest clustered design

["Michael I. Lichter" <mlichter@buffalo.edu>]

Thanks for the response, Jeph, but that's not the model I'm trying to 
fit. I'm pretty sure that I don't want random effects for condition (C) 
or time*condition (TC); those should be fixed effects only. In any 
event, without member (M) in the model, how does -xtmixed- know that I 
have two measurements per subject rather than a whole bunch of 
measurements per group (G)?
I need -xtmixed- to know that members (M) are nested within groups (G), 
and T is nested within M, and since -xtmixed- doesn't pay any attention 
to -xtset-, that needs to be in the model specification somewhere, no? 
Thanks again.

Michael

Jeph Herrin wrote:
> Try,
>
>  xtmixed y cond t tc || g: cond t tc
>
> this tells -xtmixed- to estimate random effects across group
> for cond, t, and tc.
>
> I don't know what murray means by including M as a random
> effect (and don't feel like pulling his book off the shelf
> to check), but I'm pretty sure you don't want that unless
> you have multiple records per person per time point.
>
> hth,
> Jeph
>
>
>
> Michael I. Lichter wrote:
> > I am having difficulties figuring out how to specify the random 
> > effects in -xtmixed- for my study design, and I haven't been able to 
> > find anything helpful in the archives or the manual.
> >
> > My study is a standard cluster-randomized, two-condition, 
> > two-time-point trial with balanced allocation of clusters to 
> > conditions and only moderate variation in cluster size, with no 
> > stratification, crossing, matching, or anything else. Suppose I have 
> > one record per time point per person with variables:
> >
> > c - study condition (control or intervention)
> > t - time point (pretest or post-test)
> > m - ID # for individual enrolled in trial
> > g - group #
> > y - study result
> >
> > I am taking my guidance from David Murray's DESIGN AND ANALYSIS OF 
> > GROUP-RANDOMIZED TRIALS and trying to follow his example for what he 
> > calls an "unadjusted time x condition analysis" for "nested cohort 
> > designs" (pp. 296-311). The model, with subscripts omitted, looks 
> > like this: Y = mu + c + t + tc + G + M + TG + MT + e, where mu is the 
> > grand mean, tc is the interaction effect t*c (same for TG and MT), 
> > and G, M, TG, and MT are random effects.
> >
> > Is any of these correct given the model?
> >
> > xtmixed y cond t tc || G: || M: || TG: || MT: xtmixed y cond t tc || 
> > G: TG || M: MT xtmixed y cond t tc || G: M TG MT
> > None of the above converge successfully with my data, but that 
> > doesn't mean they're all wrong ... Obviously, I'm unclear on how the 
> > specification of random effects works.
> >
> > FWIW, Murray provides the following SAS code (with my variable names; 
> > and "ddf = 4,4,4" is for a specific example):
> >
> > proc mixed info order=internal noclprint;
> >        class C G M T;
> >        model Y = C T C*T /ddf = 4,4,4 ddfm = res;
> >        repeated T /type = cs subject = M(G*C) r = 1 to 3 rcorr = 1 to 3;
> >        random G(C) TG(C);
> >        lsmeans C*T /slice=C slice=T c1 e;
> >        estimate `(I3 - I0)-(C3-C0)' C*T 1 -1 -1 1/cl e;
> > run;
> >
> > I can run this in SAS, but the value of doing so is diminished by the 
> > fact that Murray's commands and annotations are about 10 years out of 
> > date; I'd rather do it in Stata if possible.
> >
> > Thanks.
> *
> *   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/

--
Michael I. Lichter, Ph.D.
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / E-Mail: mlichter@buffalo.edu

*
*   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/