I'm running Cox proportional hazards regression in R, and would like to test the option of categorizing one of my continuous variables to factor (I'm aware of the loss of data issue, just checking).

Another thing that I'd like to check is putting the difference between 2 cont. variables inside the regression instead of putting them both.

[It's actually testing if the pulse pressure, e.g, the difference between the systolic and diastolic blood pressure is more significant than each of them separately]

My question is: What is the best way to compare between the different variations of the regressions (lets assume that I'll use step() in each of the attempts). No missing values in the dataframe whatsoever.

I'm pretty confused between AIC, R2 (of coxph) and concordance of coxph.

Can anyone clear things up for me? Is there any other option of comparing between different models on the same data?

Thanks!

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#### Best Answer

I certainly wouldn't use `step()`

here (actually, I would strongly prefer not to use it anywhere). Here I would force entry of all the variables.

Then I would use `predict`

to get predicted values from each model and compare them to the actual values, probably through a scatter plot. I might also look at the differences between the predicted and actual values, see the range for each model, perhaps make parallel box plots, then *maybe* a t-test or something like that.