Eleni Rapsomaniki wrote:> Good afternoon!
>
> I need to evaluate the goodness-of-fit (aka calibration) for survival
probability estimates from a Cox model.
> I tried to use 'calibrate' in the Design package but I'm not
sure if it should/would produce what I need (ie a chi-sq type statistic with a
table of expected vs observed probabilities). Any other functions I should be
aware of?
>
> Also, has anybody come across an implementation of the statistic described
in:
> "A global goodness of fit statistic for Cox regression models" by
Parzen & Lpisitz, Biometrics 55, 1999
>
> Many thanks in advance
>
> Eleni Rapsomaniki
Eleni,
The Design package, and its replacement, the rms package, produces
calibration curves but no chi-square test because we do not have a
corresponding method for that. Formal tests are overused in this
context anyway. An index such as the maximum or 90th percentile of
absolute calibration error are often more useful. I have learned
however that any statistical method that categorizes continuous
variables (in this case, the predictions or the covariate space) is
arbitrary and has many other problems. The calibrate functions in the
rms package have a new option to obtain smooth calibration curves
without grouping, by fitting spline hazard models during validation.
Note that if you have done any model/variable selection you have to
re-run such model building from scratch for each resample of the data,
or the calibration plot will be over optimistic. calibrate() makes this
automatic if doing backward stepdown variable selection. Many
statisticians make the mistake of only "validating" the final selected
model, which can only be done by one-time data splitting (which requires
tens of thousands of observations to perform adequately).
Frank
>
> Research Associate
> Strangeways Research Laboratory
> Department of Public Health and Primary Care
>
> University of Cambridge
>
>
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University