[R] nested cross-sectional design using lmer or nlme

[Alan Kelly]

Dear all, I'd appreciate some advice on the following problem.  I'm  
attempting to analyse a nested cross-sectional design in which an  
intervention was offered to a series of randomly selected (small)  
communities, so the unit of randomisation is the community.  All  
available individuals in each community were interviewed before the  
intervention and again at follow-up post-intervention.  The set of  
available individuals at baseline and at follow-up were far from  
identical (a common feature of such designs).  Similarly, a series of  
control communities were interviewed.  This type of design is  used in  
epidemiological studies particularly in intervention designed to alter  
lifestyle factors.  Such designs tend to be highly unbalanced Murray  
et al. discuss the appropriate analysis of such studies (Analysis of  
data from group-randomized trials with repeat observations on the same  
groups, Stats in Med. 17, 1581-1600).  They offer three examples of   
SAS code - one of which is as follow:
proc mixed;
class cond unit timecat;
  model y=cond timecat cond*timecat/ddfm=res;
  random int timecat/subject=unit(cond);
run;

cond is 0/1 corresponding to control/intervention
timecat is 0/1 corresponding to baseline/follow-up
unit is 1 to 39 and identifies the communities.
and y is a continuous score

I've read the random statement as cond nested within unit and crossed  
(?) by timecat.
Unfortunately I'm not familiar with SAS code.  I would expect random  
effects for unit and timecat X unit.

I would much appreciate any suggestions on how to code the above in  
lmer or nlme.

Alan Kelly
Trinity College Dublin
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