# [R] Evaluation of survival analysis

Mike Marchywka marchywka at hotmail.com
Tue Nov 30 01:18:16 CET 2010

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> Date: Mon, 29 Nov 2010 09:26:07 +0100
> From: hzshasha at googlemail.com
> To: r-help at r-project.org
> Subject: [R] Evaluation of survival analysis
>
> Dear all,
>
> May I ask is there any functions in R to evaluate the fitness of "coxph" and
> "survreg" in survival analysis, please?
>
> For example, the results from Cox regression and Parametric survival
> analysis are shown below. Which method is prefered and how to see that / how
> to compare the methods?

I don't know if anyone answered but personally I like to look
at pictures and relate to causality. Even the lecture slides I've
seen ultimately suggest looking at scatter plots of various residuals
for patterns. If known or suspected dynamics better fit with one
model or the other that would likely be of interest.
Generally if you pick enough parameters retrospectively you
can probably get about what ever answer you want from a quantitative
comparison.

>
> 1. coxph(formula = y ~ pspline(x1, df = 2))
>
> coef se(coef) se2 Chisq DF
> p
> pspline(x1, df = 2), line 0.0522 0.00867 0.00866 36.23 1.00 1.8e-09
> pspline(x1, df = 2), nonl 3.27 1.04
> 7.5e-02
>
> Iterations: 4 outer, 13 Newton-Raphson
> Theta= 0.91
> Degrees of freedom for terms= 2
> Likelihood ratio test=34.6 on 2.04 df, p=3.24e-08
>
> 2. survreg(formula = y ~ pspline(x1, df = 2))
>
> coef se(coef) se2 Chisq DF
> p
> (Intercept) 2.8199 0.15980 0.09933 311.37 1.0 0.0e+00
> pspline(x1, df = 2), line -0.0193 0.00248 0.00248 60.35 1.0 8.0e-15
> pspline(x1, df = 2), nonl 1.43 1.1
> 2.6e-01
>
> Scale= 0.304
>
> Iterations: 6 outer, 20 Newton-Raphson
> Theta= 0.991
> Degrees of freedom for terms= 0.4 2.1 1.0
> Likelihood ratio test=48.2 on 1.5 df, p=1.18e-11
>
>
> I really appreciate for your help. Thank you very much in advance.
>
> Best wishes,
> He

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