[R] relative risk regression with survey data

[Daniel Nordlund]

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 <tlumley at 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
>
>