Thanks to Thomas Lumley and David Winsemius for their responses. I had read a number of papers by Thomas and have ordered his book on survey analysis, but I wanted to get some confirmation because I wanted to get started before the book arrived. Thanks, again.
Dan Daniel Nordlund Bothell, WA USA On Mon, Sep 13, 2010 at 7:40 PM, Thomas Lumley <tlum...@u.washington.edu> wrote: > On Mon, 13 Sep 2010, Daniel Nordlund wrote: > >> I have been asked to look at options for doing relative risk regression on >> some survey data. I have a binary DV and several predictor / adjustment >> variables. In R, would this be as "simple" as using the survey package to >> set up an appropriate design object and then running svyglm with >> family=binomial(log) ? Any other suggestions for covariate adjustment of >> relative risk estimates? Any and all suggestions welcomed. > > If the fitted values don't get too close to 1 then svyglm( > ,family=quasibinomial(log)) will do it. > > The log-binomial model is very non-robust when the fitted values get close > to 1, and there is some controversy over the best approach. You can still > use svyglm( ,family=quasibinomial(log)) but you will probably need to set > the number of iterations much higher (perhaps 200). > > Alternatively, you can use nonlinear least squares [svyglm(, > family=gaussian(log))] or other quasilikelihood approaches, such as > family=quasipoisson(log). These are all consistent for the same parameter > if the model is correctly specified and are much more robust to x-outliers. > I rather like nonlinear least squares, because it's easy to explain. > > -thomas > > > Thomas Lumley > Professor of Biostatistics > University of Washington, Seattle > > ______________________________________________ R-help@r-project.org 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.
