Hello everyone,
I have a dataset of diabetic patients which has been used to train an xgboost model in several outcomes such as stroke, amputation, and more. Originally we used the continuous numeric variables as-is, but we found ambiguity in the results since for example age was giving us results where the older you get the higher the risk to have a stroke.
But, for us as physicians we need a narrower range, so we divide those variables in bins. And indeed this gave us more insight. Nonetheless, we are seeing that some contiguous intervals appear in our results pretty close.
Continuing from the example above, bin(64-78) and bin(79-88), appear one after the other and no other bin from the age variable appears. So we think that the best approach, in this case, is to find the best optimal cutpoint at which the age starts to become a risk factor for stroke.
Then I came across this document (https://www.mayo.edu/research/documents/biostat-79pdf/doc-10027230) which explains in SAS how to find those cutpoints. I am not experienced enough to program this myself, so I want to know how could I achieve to find these cutpoints in python?
Do please restrict to that language, I have already seen R, SAS, even SPSS examples but none in python. There must be some way to do this in Python.
if I understood your q you want to separate some variable; you can do it either using feature selection methods or drop out the least important feature such as
df=df.drop('TimeStamp', axis=1)
or you can do it easily using the weka tool
no, i want to split the values in one variable in only two, lets say 0 and 1, but that rule should come from some ROC curve or PR curve where the cut off point is the best for sensitivity and specificity. So that when i train the model i can calculate the RR for that cut off point, lets say if cut off for age is 44, then everything above is labeled as 1 and the rest is 0, then my RR would be that patients with more than 44 years of age have N more or less probability to develop some condition like stroke
You may want to use 'cutp' from the 'survmisc' package in R to find the best age cutoff. Perform a cox survival analysis and see which age cutoff discriminates the risk groups better (max HR and min p-value).
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