Dear Thomas, You said, "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." Could you please point me to a paper that discusses the issues?
I have written some code to do maximum likelihood estimation for relative, additive, and mixed risk regression models with binomial model. I have been able to obtain good convergence. I have used bootstrap to get standard errors. However, I am not sure if these standard errors are valid when fitted values were close to 0 or 1. It seems to me that when the fitted probabilities are close to 0 or 1, there is not a good way to estimate standard errors. Thanks, Ravi. -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Thomas Lumley Sent: Monday, September 13, 2010 10:41 PM To: Daniel Nordlund Cc: r-help@r-project.org Subject: Re: [R] relative risk regression with survey data 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. ______________________________________________ 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.
