I just fitted a boosted regression coxph model:

`cox=gbm(Surv(periods, event) ~ grade + fico_range_low + revol_util + dti, data=notes) `

However, I want to obtain the survival curve from the model similar to the `survfit()`

function in the `survival`

package. Does anyone know how to obtain using the model from the `gbm`

package?

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#### Best Answer

Since package `gbm`

requires package `survival`

and can use the `Surv()`

function in the canonical formula interface, and there is also a reference to `survfit()`

in the `basehaz.gbm()`

documentation in the gbm vignette, it might be possible to pull out what you're looking for using the gbm model object. I doubt it, though, based on what I've read so far.

So you might have to go to the source code and make your own function to extract or reconstruct what you need to mimic `plot(survfit())`

. See also the (hidden) helper function `reconstructGBMdata()`

.

I also wanted to comment on using gradient boosting models, specifically for handling missing values. The short answer here is that the gbm algorithm handles missing values explicitly, obviating the need for user-handled imputation. I suggest looking up package rpart if you want to understand the technical details better.

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