The dataset contains info about 146 satge C prostate cancer patients. 
The main clinical endpoint of interest is whether the disease recurs 
after initila surgical removal of the prostate, and the time interval 
to that progression (if any). The enpoint of this example is pgstat, 
which takes on the value 1 if the disease has progressed and 0 if not. 
Below is a short description of the variables.

The data is a matrix of 146 rows and 8 columns corresponding to the
following 8 variables:

pgtime = time to progression in years
pgstat = status at last follow-up: 1=progressed, 0=censored
age = age at diagnosis
eet = early endocrine therapy: 1=no 2=yes
g2 = % of cells in g2 phase, from flow cytometry
grade = tumor grade 1,2,3,4
gleason = Gleason score (competing grading system, 3-10)
ploidy = diploid/tetraploid/aneuploid DNA pattern

We will first try to predict pgstat from the last 6 variables (age, eet,
g2, grade, gleason, ploidy)