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Here is a stratified model build based on the tiny test set you offered library(survival) gender <- as.factor(c("Female", "Male", "Female", "Male", "Male", "Male", "Female", "Female", "Female", "Male") … Therneau & Gambsch's text "Modeling Survival Data" has an excellent chapter. …

The "absolute risk" is the events per unit time divided by the number of individuals susceptible to the event under observation. It is specifically not the value of S(t). During periods of no risk the …

I think you might look for modeling frameworks where clients are in a few different states (perhaps: new user, established-user, established-new-problem, frustrated-user) and use survival analysis to … The survival package in R (which is what your citation was using) also has a multi-state outcome type, but it's description seems to imply that various states are either censored or "absorbing" (in the …

This is a bit likethe data presentations done by oncologists and transplant surgeons where Kaplan-Meier plots of "progression-free survival" or "rejection-free survival" are presented, but when you read … It rather stands the meaning of "survival" on its head. The specialists are throwing up their hands and saying "not my fault!" …

Harrell would advise that you NOT do so: How to do ROC-analysis in R with a Cox model Doing model comparison with LR statistics is more powerful than using methods that depend on an asymptotic distr …

Looking at both Therneau and Gramsch "Modeling Survival Data" and Kalbfleisch and Prentice "Statistical Analysis of Failure Time Data", the definition of S(t) is Pr(T>t) and all of their interval survival …

(I don't understand why you didn't pose the question using one of the available datasets in the 'survival' package.) … In the survival package there is also a survfit function that can generate KM-like curves from model-objects that with no newdata argument will be the estimated survival function determined at the mean …

Shortening the time interval in this case will not reduce the power of test because statistical power in survival analysis comes from the numbers of events rather than the duration of the study. …

The methods description does not match up with anything I see in Crawley's chapter on survival analysis. … I do not see that the multiple comparisons problem is specific to survival analysis. …

It's fairly common for authors to notice and comment in the text of an article that the median time to failure or relapse is a particular interval. Of course, not all studies get to the point where th …

I think it would be more accurate to say that: $l(\theta) = \sum( \log(\theta) )- \sum( \theta Y_i)$ $l(\theta) = n_u \log(\theta) )- \sum( \theta Y_i)$ where u's are uncensored and Y's are observa …

For further information on how the authors of the survival package use cox.zph, I recommend Chapter 6:"Testing Proportional Hazards" in their book, "Modeling Survival Data". …

As far as R programming goes, the formula you offer is equivalent to: s ~ age * treatment The asterisk-operator is overloaded in R. When its arguments are numeric, it is multiplication, and when t …

Looks like you have a predictor that varies cyclically. My impression from very limited reading of the botanical literature is that something along the lines of cumsum(degree_days) (where the sums are …

The help page for ?Weibull says: The Weibull distribution with shape parameter a and scale parameter b has density given by f(x) = (a/b) (x/b)^(a-1) exp(- (x/b)^a) And then the help page fo …