R Dataset / Package survival / bladder
On this Picostat.com statistics page, you will find information about the bladder data set which pertains to Bladder Cancer Recurrences. The bladder data set is found in the survival R package. Try to load the bladder data set in R by issuing the following command at the console data("bladder"). This may load the data into a variable called bladder. If R says the bladder data set is not found, you can try installing the package by issuing this command install.packages("survival") and then attempt to reload the data with library("survival") followed by data("bladder"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing bladder at the command-line which should display the entire dataset.
If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the bladder R data set. The size of this file is about 5,385 bytes.
Bladder Cancer Recurrences
Data on recurrences of bladder cancer, used by many people to demonstrate methodology for recurrent event modelling.
Bladder1 is the full data set from the study. It contains all three treatment arms and all recurrences for 118 subjects; the maximum observed number of recurrences is 9.
Bladder is the data set that appears most commonly in the literature. It uses only the 85 subjects with nonzero follow-up who were assigned to either thiotepa or placebo, and only the first four recurrences for any patient. The status variable is 1 for recurrence and 0 for everything else (including death for any reason). The data set is laid out in the competing risks format of the paper by Wei, Lin, and Weissfeld.
Bladder2 uses the same subset of subjects as bladder, but formatted in the (start, stop] or Anderson-Gill style. Note that in transforming from the WLW to the AG style data set there is a quite common programming mistake that leads to extra follow-up time for 12 subjects: all those with follow-up beyond their 4th recurrence). Over this extended time these subjects are by definition not at risk for another event in the WLW data set.
bladder1 bladder bladder2
Andrews DF, Hertzberg AM (1985), DATA: A Collection of Problems from Many Fields for the Student and Research Worker, New York: Springer-Verlag.
LJ Wei, DY Lin, L Weissfeld (1989), Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. Journal of the American Statistical Association, 84.
Dataset imported from https://www.r-project.org.
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