Greetings ~

I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R.

The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems (yolk sac, jaw, snout, body axis, pericardial edema, etc.) for each fish. The assay can be used as a screen (in which case a single response 'any' is first analyzed) as well as for comparing the responses of different classes of chemicals (a MANOVA-type analysis). Interest is focused on where response(s) are occurring, any associations among responses, and ultimately on possible biological mechanisms (the fish are tested for behavioral activity prior to this assay, and then ground up to provide RNA for microarray assays. A-lotta-data!).

What I *wish* I could do is something like glm(response.matrix ~ treat/vial, family=binomial(logit), data=zf.dat) but I know this can't be done. I came across the baymvb (Bayesian analysis of multivariate binary data) package in the R contributed packages archives, but it is no longer supported and the author is incommunicado. In the baymvb function the model is specified as single.stacked.response ~ structure.factor + linear.model.terms. Huh? This looks suspiciously similar to analyzing repeated measures data in SAS as a univariate response with a split-plot structure (which forces the response covariance matrix to have a compound symmetric structure). If this is what's happening with this function it is definitely not appropriate. How about a GEE approach (I'm not familiar with the literature, or with any of the R packages that implement it)? Any other recommendations? (NB: just discovered the bayesm package. Don't know if this will work for me or not.)

Any help would be greatly appreciated.
Derek Janszen, PhD
Sr Research Biostatistician
Computational Biology & Bioinformatics
Pacific Northwest National Laboratory
Richland, WA  99352 USA
derek.janszen@pnl.gov



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