search for: gramsch

Dear All, Are there R packages that can estimate survival model with long-term survivors? This is sometimes known as "cure" model or "split-population" model. Thanks. Shige [[alternative HTML version deleted]]

Hi All, I am trying to figure out how to get the position of the knots in a pspline used in a cox model. my.model = coxph(Surv(agein, ageout, status) ~ pspline(x), mydata) # x being continuous How do I find out where the knot of the spline are? I would like to know to figure out how many cases are there between each knot. Best, Federico -- Federico C. F. Calboli Department of Epidemiology

Hello , I am hoping for some advice regarding obtaining the values for the hazard function in a cox regression that I have undertaken. I have a model in the following form, analysed with the package survival (v. 2.34-1) and a log-log plot obtained using Design (v. 2.1-2). For two variables, the lines in the survival curves crossed. The statistician I been obtaining advice from (who does not

Hi list, I was trying to use "predict.coxph" to calculate martingale residuals on a test data, however, as pointed out before http://tolstoy.newcastle.edu.au/R/e4/help/08/06/13508.html predict(mycox1, newdata, type="expected") is not implemented yet. Dieter suggested to use 'cph' and 'predict.Design', but from my reading so far, I'm not sure they can

Dear All, Sorry to bother you again. I have a model: coxfita=coxph(Surv(rem.Remtime/365,rem.Rcens)~all.sex,data=nearma) and I'm trying to do a plot of Schoenfeld residuals using the code: plot(cox.zph(coxfita)) abline(h=0,lty=3) The error message I get is: Error in plot.window(...) : need finite 'ylim' values In addition: Warning messages: 1: In sqrt(x$var[i, i] * seval) : NaNs

...; > >>>> I was trying to use "predict.coxph" to calculate martingale residuals on >a > >> test > >>>> data, however, as pointed out before > >>> > >>> What about resid(fit) ? It's my reading of Therneau & Gramsch [and of > >> help(coxph.object) ] that they consider those martingale residuals. > >> > >> The manner in which I _thought_ this would work was to insert some dummy >cases > >> into the original data and then to get residuals by weighting the cases &gt...