Dear Sir/Madam,
I am a PhD student at the dept. of Epidemiology, Biostatistics and HTA
of the Radboud University Nijmegen Medical Centre, Nijmegen, The
Netherlands. My project is about the role of genetic variants in the
prognosis of urinary bladder cancer. For my project, I'm currently
analyzing my data with survival tree analysis using the rpart package
available in R.
I succeeded in performing survival tree analysis with this package using
the "method=exp" function, and by defining a "Surv object". However, I
wonder what kind of splitting algorithm and pruning method are used by R
to obtain the survival tree.
I know that for classification trees, it is possible to specify the
split algorithm using the function "parms=list(split=...)", which can be
specified as 'gini' or 'info'. Are there also multiple options for the
splitting algorithm using the "Exp" method for survival trees, that can
be defined or is there only one default option?
Is the pruning method for the survival tree in the rpart package also
based on tree cost complexity?
Is it possible, and if so, how do I create a user-defined splitting and
pruning algorithm for survival tree analysis?
I have read the report "An introduction to recursive partitioning using
the rpart routines - Therneau and Atkinson". It was really helpful in
familiarizing myself with CART analysis using the rpart package.
However, I missed some detail on the background of survival tree
analysis (using rpart and in general). Can anyone inform me whether
there is another document or book that elaborates more on this subject?
Thank you very much in advance.
Yours sincerely,
Anne Grotenhuis.
Het UMC St Radboud staat geregistreerd bij de Kamer van Koophandel in het
handelsregister onder nummer 41055629.
The Radboud University Nijmegen Medical Centre is listed in the Commercial
Register of the Chamber of Commerce under file number 41055629.
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