Dear R-listers, 
 
I am fitting bivariate mixed models for cost-effectiveness data of cluster randomized trials using lme in R.  So I have individuals nested within clusters. My response variable is a vector with bivariate response (individual level costs and effects) stacked into a single column. The covariates in my models are a constant and a treatment term. They are  response-specific, e.g. a constant for cost (cons.1) and one for effect (cons.2) , a treatment term for cost (treat.1) and one for effect (treat.2). The syntax I used is this: 
 
lme(yl~ 
 -1+cons.1+treat.1+cons.2+treat.2,
 random=~-1+cons.1+cons.2|cidl,
 weights=varIdent(form=~1|cons.1),
 corr=corCompSymm(form=~1|cidl/indl), 
 data=datalong)
 
where cidl = cluster id, indl = individual id and datalong = data (also data had been stacked before model fitting)
 
I have been trying to extend this model to allow individual level standard deviations and correlations to differ by cluster. In my data, there is evidence that SDs for costs and effects and correlations between costs and effects 
differ by cluster. I have worked out the code to account for the former but not both (see code below).  
 
lme(yl~ 
 -1+cons.1+treat.1+cons.2+treat.2,
 random=~-1+cons.1+cons.2|cidl,
 weights=varIdent(form=~1|resp.cidl),
 corr=corCompSymm(form=~1|cidl/indl), 
 data=datalong,
 control=lmc2) # SANN: 20k iterations 
 
where resp.cidl = response-specific cluster id, lmc2 = a lmcControl object specifying SANN as optimisation method among other settings 
 
I would like to find out if there is a way to allow for the correlations to differ by cluster via the corr option. Any suggestion or pointer would be greatly appreciated. 
 
Many thanks in advance. 
 
Best wishes, 
Edmond 
 
 
 
 
 
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Edmond Ng, MSc CStat CSci
Lecturer in Medical Statistics
Department of Health Services Research and Policy, Faculty of Public Health and Policy
London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK 
Tel: +44 (0)20 7927 2366 (direct line: ext 2065)  Fax: +44 (0)20 7927 2701
Website: http://www.lshtm.ac.uk/hsru
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