Dear Terry Thanks for your reply, I guess then including only the interaction term only should make sense, since a difference in group 1 between t=0 and t=1 is already taken into account in the baseline (all coefficients set to zero). My second group as I understand it should have an exp(coef_group2) higher/lower hazard at t=0, and exp(coef_group2+coef_group2:t) higher at t=1 as compared to the baseline. Am I correct so far?.
I have a second question that might be off topic, but though I'd have a fair chance off getting an answer at this mailing list. If I would assume a more or less constant hazard within periods 1 and 2, how would I get an average "daily" survival for each period on the probability scale? I thought of two options, 1 take the cumulative survival probability at time 13 (= my cutpoint) tmax, and take the 13- and tmax- th root of it, or divide the ratio of S(t)/S(t-1) with the corresponding time difference between the two events, and then average over groups and periods. However , what would an appropriate method be to estimate the standard errors of the daily survival/ failure probabilities? Would the delta method do? And if so, does anyone known of a reproducible example? Any help is very much appreciated Thank in advance Caspar Caspar Hallmann MSc Student WUR The Netherlands ----- Original Message ----- From: "Terry Therneau" <[EMAIL PROTECTED]> To: <[EMAIL PROTECTED]> Cc: <r-help@r-project.org> Sent: Friday, October 19, 2007 3:30 PM Subject: Re: X matrix deemed to be singular in counting process coxph > What you have is a slightly more subtle variant of the following: > > library(survival) > data(lung) > mydata <- cbind(lung, newvar =2) > coxph(Surv(time, status) ~ ph.karno + newvar, mydata) > > coef exp(coef) se(coef) z p > ph.karno -0.0164 0.984 0.00585 -2.81 0.005 > newvar NA NA 0.00000 NA NA > > > You have created a data set where at all times <13 the variable t=0, and > at all > times >13 the variable t=1. The Cox model compares the values of the > covariates > of each subject who died to the values of those who did not die, using the > current covariate values AT THAT TIME. Since the value of your "t" is > always a > constant within the set, the variable contains no information for > discriminating > the events from the non-events. Zero information --> a coefficient of NA. > > Terry Therneau > Mayo Clinic ______________________________________________ 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.
