---------- Forwarded message ---------- From: He Zhang <hzsha...@googlemail.com> Date: Tue, Nov 30, 2010 at 11:26 AM Subject: Re: [R] Evaluation of survival analysis To: Mike Marchywka <marchy...@hotmail.com> Cc: r-help@r-project.org
On Tue, Nov 30, 2010 at 1:18 AM, Mike Marchywka <marchy...@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: hzsha...@googlemail.com > > To: r-help@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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