Hi all, Sorry to ask again but I'm still not sure how to get the full variance-covariance matrix. Peter suggested a three-level treatment factor. However, I thought that the censoring variable could only take values 0 or 1 so how do you programme such a factor.
Alternatively, is there another way to produce the required covariance? Thank you, Laura On Tue, Aug 26, 2008 at 11:37 AM, Laura Bonnett <[EMAIL PROTECTED]>wrote: > The standard treatment is the same in both comparison. > > How do you do a three-level treatment factor? > I thought you had to have a censoring indicator which took values 0 or 1 > not 1, 2 or 3? > > Thanks, > > Laura > > On Tue, Aug 26, 2008 at 11:05 AM, Peter Dalgaard <[EMAIL PROTECTED] > > wrote: > >> Laura Bonnett wrote: >> > Dear R help forum, >> > >> > I am using the function 'coxph' to obtain hazard ratios for the >> comparison >> > of a standard treatment to new treatments. This is easily obtained by >> > fitting the relevant model and then calling exp(coef(fit1)) say. >> > >> > I now want to obtain the hazard ratio for the comparison of two >> non-standard >> > treatments. >> > >From a statistical point of view, this can be achieved by dividing the >> > exponentiated coefficients of 2 comparisions. E.g. to compared new >> treatment >> > 1 (nt1) to new treatment 2 (nt2) we can fit 2 models: >> > fit1 = standard treatment vs nt1 >> > fit2 = standard treatment vs nt2. >> > The required hazard ratio is therefore exp(coef(fit1))/exp(coef(fit2)) >> > >> > In order to obtain an associated confidence interval for this I require >> the >> > covariance of this comparison. I know that R gives the >> variance-covariance >> > matrix by the command 'fit$var'. However, this only gives the >> covariance >> > matrix for non standard drugs and not the full covariance matrix. >> > >> > Can anyone tell me how to obtain the full covariance matrix? >> > >> > >> What kind of data do you have? Is the "standard treatment group" the >> same in both comparisons? If so, why not just have a three-level >> treatment factor and compare nt1 to nt2 directly. If the control groups >> are completely separate, then the covariance between fits made on >> independent data is of course zero. >> >> > Thank you, >> > >> > Laura >> > >> > [[alternative HTML version deleted]] >> > >> > ______________________________________________ >> > 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. >> > >> >> >> -- >> O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B >> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K >> (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 >> ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 >> >> >> > [[alternative HTML version deleted]]
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