[R-sig-ME] nested cross-sectional model using lme4 or nlme
akelly at tcd.ie
Fri Jun 26 08:15:40 CEST 2009
Dear all, following a suggestion from one of your contributors, I'm
posting the following query to R-sig-mixed having had limited response
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 was 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 interventions 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:
class cond unit timecat;
model y=cond timecat cond*timecat/ddfm=res;
random int timecat/subject=unit(cond);
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
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
lme4 or nlme.
Trinity College Dublin
More information about the R-sig-mixed-models