Dear List, I am (again) looking at meta-regression as a way to refine meta-analytic results. What I want to do is to assess the impact of some fixed factors on the results of a meta-analysis. Some of them may be crossed with the main factor of the meta-analysis (e. g. clinical presentation of a disease, defining subgroups in each of the studies under analysis), some of them may be a grouping factor amond studies (e. g. study design characteristics).
Homework : the R packages meta and Rmeta do not allow for multiple independent factors. Looking hard at R-help archives, I found one of my previous posts on this subject (which got me an answer suggesting using lme()), a discussion of lme() use concluding that the R version of lme() could not be used for this purpose, and a 2002 discussion ending up with a custom function for (part of) this purpose ([EMAIL PROTECTED], Sat 31 Aug 2002 - 06:31:11 EST). Now, lmer() introduced a new method of specification of the variance sources (it has also the advantage of being usable with various models, such as linear, binomial or Poisson). However, I have been unable to extract from lmer() documentation a way to specify variances associated with each individual mean. I might also use some help for the specification of variance structures. In short, what I want to be able to do is to write something along the lines of : analysis<-foo( Outcome~Treatment*(Presentation+Design), random=(1|Study %in% Design), sd=Sds data=RawData, ...) where RawData is a data frame with a line for each level of Study*Treatment*Presentation, each study having only one value for Design ; Outcome is the mean value of the judgement criterion in the corresponding subgroup of the study, and Sds is its standard deviation. The results of interests are mainly the effects of - Treatment (of course) - Treatment:Presentation (of great clinical interest) - Treatment:Design (is there evidence of bias introduced by study design ?) Of secondary interest are : - Presentation (i. e. natural history of the disease) - Design (How should future studies be done ?) Any hint on writing foo() using lmer() (or otherwise) and using the resulting object would be very much appreciated. Emmanuel Charpentier PS : a CC to [EMAIL PROTECTED] would also be appreciated : I'm reading the list through the web archives. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.