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:
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)*
*> Option.test2<-coxph(Surv(START,STOP,ZEROBAL==1)~pspline(OPTION),
data=FNMA)coxph(formula = Surv(START, STOP, ZEROBAL == 1) ~
pspline(OPTION), data = FNMA)> > Option.test2> Call:> coxph(formula =
Surv(START, STOP, ZEROBAL == 1) ~ pspline(OPTION), > data = FNMA)>
coef se(coef) se2 Chisq DF
p> pspline(OPTION), linear -0.1334 0.0131 0.0131 104.4325
1.00 <0.0000000000000002> pspline(OPTION), nonlin
1747.1295 3.05 <0.0000000000000002> Iterations: 8 outer, 19
Newton-Raphson> Theta= 0.991 > Degrees of freedom for terms= 4 >
Likelihood ratio test=2136 on 4.05 df, p=0 n= 3390429 > *
Thanks,
KS
--
Kevin E. Thorpe
Head of Biostatistics, Applied Health Research Centre (AHRC)
Li Ka Shing Knowledge Institute of St. Michael's Hospital
Assistant Professor, Dalla Lana School of Public Health
University of Toronto
email: kevin.tho...@utoronto.ca Tel: 416.864.5776 Fax: 416.864.3016
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