On 11/17/05, Christoph Scherber <[EMAIL PROTECTED]> wrote: > Dear list, > > I have data on insect survival in different cages; these have the > following structure: > > deathtime status id cage S F G L S > 1.5 1 1 C1 8 2 1 1 1 > 1.5 1 2 C1 8 2 1 1 1 > 11.5 1 3 C1 8 2 1 1 1 > 11.5 1 4 C1 8 2 1 1 1 > > There are 81 cages and each 20 individuals whose survival was followed > over time. The columns S,F,G,L and S are experimentally manipulated > factors thought to have an influence on survival. > > Using survfit(Surv(deathtime,status)~cage) gives me the survivorship > curves for every cage. But what I´d like to have is a mean survivorship > value for every cage. > > Obviously, using tapply (deathtime,cage,mean) gives me mean values, but > I´d like to have a better estimate of this using a proper statistical > model. I´ve tried a glm with poisson errors (as suggested in Crawley´s > book, page 628), but the back-transformed estimates (using status as the > response variable and deathtime as an offset) were totally unrealistic. > > As I´m new to survival analysis, it would be great if anyone could give > me some hints on what method would be best.
No method is best, but some methods may be useful ;) One such may be to fit a parametric model to your data. Check 'survreg'. Göran > > Thanks a lot! > Christoph > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > -- Göran Broström ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
