[R] Evaluation of survival analysis
He Zhang
hzshasha at googlemail.com
Tue Nov 30 11:26:20 CET 2010
On Tue, Nov 30, 2010 at 1:18 AM, Mike Marchywka <marchywka at hotmail.com>wrote:
>
>
> Hello Mike,
>
Thank you very much for your reply and help.
May i describe the analysis more clearly?
My data is ecology data and my task is to 1) relate the 8 candidate (life
history) varaibles with the lifespan of each subject and 2) use the known
variables to predict lifespan.
For the 1st task, i used Cox regression "coxph()" to do uni-variate
analysis first. However, the most variables are correlated with each. For
involving more variables, principle component analysis is applied. After PAC
"principal()", I chose three vairalbes according to the results (instead of
the derived principle components since the interpretation of the original
variables is easier) .
For the 2nd task, i wanted to use the chosen variables to predict the
lifespan. "predict(survreg())" can get the values.
I attached parts of the results which are the residuals plot and predcited
values vs. predictors derived from both Cox regression and parametric
survival.
My problem: 1) not sure if the methods are correct for the tasks since the
residuals plots are not totally randomly and the predicted hazard is less
than 0. 2) i dont know how to explain the fitness of the model.
Any suggestion about the methods or results will be really appreciate. Thank
you again.
Best wishes,
He
>
>
>
>
>
> ----------------------------------------
> > 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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