> On examining non-linearity of Cox coefficients with penalized splines - I
> have not been able to dig up a completely clear description of the test
> performed in R or S-plus.
One "iron clad" way to test is to fit a model that has the variable of
interest
"x" as a linear term, then a second model with splines, and do a likelihood
ratio test with 2*(difference in log-likelihood) on (difference in df) degrees
of freedom. With a penalized model this test is conservative: the chi-square
is
not quite the right distribution, the true dist has the same mean but smaller
variance.
The pspline function uses an evenly spaced set of symmetric basis functions.
A
neat consequence of this is that the Wald test for linear vs 'more general' is
a
test that the coefficients of the spline terms fall in a linear series. That
is, a linear trend test on the coefficients. This is what coxph does. As with
the LR test, the chi-square dist is conservative. I have not worked at putting
in the more correct distribution. See Eilers and Marx, Statistical Science
1986.
> And what is the null for the non-linear test?
The linear test is "is a linear better than nothing", the non-linear one is a
sequential test "is the non-linear better than the linear". The second test of
course depends on the total number of df you allowed for the pspline fit. As a
silly example adding "+ pspline(x, df=200)" would likely show that the
nonlinear
term was not a significant addition, i.e., not worth 199 more degrees of
freedom.
Terry Therneau
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