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.
