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Cox Regression : Spline Coefficient Interpretation?

3 messages · Kosta S., Bert Gunter, Kevin E. Thorpe

Original
Kosta S.
# 
Hi,

I'm using a Cox-Regression to estimate hazard rates on prepayments.

I'm using the "pspline" function to face non-linearity, but I have no clue
how to interpret the result.
Unfortunately I did not find enough information on the "pspline" function
wether in the survival package nor using google..

I got following output:

* library(survival)*
Thanks,

KS
Bert Gunter
# 
??

It is unclear to me what "How to interpret the result" means. Note that the
survival package is very well documented and there is a vignette
specifically on the topic of the use of "Spline terms in a Cox model." Have
you studied it?

If you want to discuss the statistical issues, e.g. of survival modeling
or the technical details of penalized smoothing splines, that is mostly OT
here: stats.stackexchange.com would probably be a better place to post for
that. This list is mostly about R programming rather than statistics,
although they do sometimes intersect.

If I have misunderstood your question, you might wish to clarify exactly
what it is that you are seeking in another post.

Finally, as you can see from the below, post in PLAIN TEXT ONLY, as html
can get mangled by the server on this plain text mailing list.

Cheers,
Bert



Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, Nov 1, 2017 at 1:12 PM, Kosta S. <kosmirnov at gmail.com> wrote:

            

      
      
      
    

        

      
      
    

        

  
  


  


      

    

    

      
      

        


  

        

      Kevin E. Thorpe
    

    

      
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Your output is mangled beyond interpretation.

However, when it comes to interpreting splines in general, you cannot 
easily convert the individual beta coefficients into, say HR by 
exponenitating them. The collection of beta coefficients describe the 
relationship between the continuous variable and the outcome.

Consider a simple case. Suppose you fit a model with x and x^2. You 
cannot really interpret the x^2 coefficient in isolation from the x 
coefficient. It is the same with splines only worse.

Graphical displays of the spline are often more informative.

Kevin

        
On 11/01/2017 04:12 PM, Kosta S. wrote: