[R] [follow-up] "Longitudinal" with binary covariates and outcome

[Charles C. Berry]

Ted,

What you have can be rendered as a 2^5 (X1 by X2 by X3 by X4 by Y) table 
of counts, right?

Why isn't this a vanilla log-linear modelling (as in loglin() ) problem?

It seems to me that the temporal aspect you describe suggests a sequence 
of margins that could be studied, viz

 	list( 1:4, c(4,5) )
 	list( 1:4, c(3,5), c(4,5) )
 	list( 1:4, c(2,5), c(3,5), (4,5) )
 	list( 1:4, c(1,5, c(2,5), c(3,5), (4,5) )

(taking X1 is the first and Y as the last slice in the table)

and perhaps intercalating higher order effects involving slice 5 amongst 
those.

??

Chuck

On Wed, 12 Mar 2008, Ted.Harding at manchester.ac.uk wrote:

> Hi again!
> Following up my previous posting below (to which no response
> as yet), I have located a report which situates this type
> of question in a longitudinal modelling context.
>
> http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps
> Generalized Linear Models with Longitudinal Covariates
> Daowen Zhang & Xihong Lin
>
> (This work seems to originally date from around 1999).
>
> They consider an outcome Y, with a fixed covariate [vector] Z
> and a longitudinal covariate [vector] X observed at n time
> points t1,...,tn; the outcome Y is observed only at the end
> of the sequence. They model Y with a GLM in which Z and
> subject-specific random effects U are predictors in the GLM,
> where U satisfies a linear mixed model X = T'*U + error
> and is normally distributed.
>
> However, in view of the fact that the longitudinal covariates
> X in my query below are binary, there cannot be a linear
> mixed model for them; there would have to be a generalised
> linear mixed model.
>
> I have had a good poke around in the R resources, and have
> failed to find anything which directly addresses this question
> (nor which addresses Zhang & Lin's original question).
>
> So, if anyone has done R work in this kind of context,
> I'd be most grateful for any suggestions (including worked
> examples of datasets) arising from it!
>
> With thanks again, and best wishes to all,
> Ted.
>
>
> -----FW: <XFMail.080311001718.Ted.Harding at manchester.ac.uk>-----
> Date: Tue, 11 Mar 2008 00:17:18 -0000 (GMT)
> From: (Ted Harding) <Ted.Harding at manchester.ac.uk>
> To: r-help at stat.math.ethz.ch
> Subject: "Longitudinal" with binary covariates and outcome
>
> Hi Folks,
> I'd be grateful for suggestions about approaching the
> following kind of data. I'm not sure what general class of
> models it is best situated in (that's just my ignorance),
> and in particular if anyone could point me to case studies
> associated with an R approach that would be most useful.
>
> Suppose I have data of the following kind. Each "subject"
> is observed at say 4 time-points T2, T2, T3, T4, yielding
> values of binary (0/1) variables X1, X2, X3, X4. At time T4
> is also observed a binary variable Y. The objective is to
> study the predictive power of (X1, X2, X3, X4) for the
> outcome "Y=1".
>
> A useful model should take account of the possibility
> that more "recent" X's are likely to be better predictors
> than less "recent" so that, say, P(Y=1|X4=1) is likely to
> be larger than P(Y=1|X1=1), and also that the more X's
> are 1, the more likely it is that Y=1.
>
> Any suggestions or comments and, as I say, pointers to
> an R treatment of similar problems would be most welcome.
>
> With thanks,
> Ted.
> --------------End of forwarded message-------------------------
>
> --------------------------------------------------------------------
> E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
> Fax-to-email: +44 (0)870 094 0861
> Date: 12-Mar-08                                       Time: 14:35:59
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Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901