R help - Jun 2010 - Probabilities from survfit.coxph:

Hello:

In the example below (or for a censored data) using survfit.coxph, can
anyone point me to a link or a pdf as to how the probabilities appearing in
bold under "summary(pred$surv)" are calculated? Do these represent
acumulative probability distribution in time (not including censored time)?

Thanks very much,
parmee

*fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*
*pred <- survfit(fit, newdata=data.frame(age=60))*
*summary(pred)*

 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   59     26       1    *0.978*  0.0240        0.932        1.000
  115     25       1    *0.952*  0.0390        0.878        1.000
  156     24       1    *0.917*  0.0556        0.814        1.000
  268     23       1    *0.880*  0.0704        0.752        1.000
  329     22       1    *0.818*  0.0884        0.662        1.000
  353     21       1    *0.760*  0.0991        0.588        0.981
  365     20       1    *0.698*  0.1079        0.516        0.945
  431     17       1    *0.623*  0.1187        0.429        0.905
  464     15       1    *0.549*  0.1248        0.352        0.858
  475     14       1    *0.480*  0.1267        0.286        0.805
  563     12       1    *0.382*  0.1332        0.193        0.757
  638     11       1    *0.297*  0.1292        0.127        0.697

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On Wed, Jun 23, 2010 at 9:03 PM, Parminder Mankoo <pkmankoo09 at
gmail.com> wrote:
> Hello:
>
> In the example below (or for a censored data) using survfit.coxph, can
> anyone point me to a link or a pdf as to how the probabilities appearing in
> bold under "summary(pred$surv)" are calculated? Do these
represent
> acumulative probability distribution in time (not including censored time)?
predicted survival. Hence the column name "survival"...

Cheers
Joris
>
> Thanks very much,
> parmee
>
> *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*
> *pred <- survfit(fit, newdata=data.frame(age=60))*
> *summary(pred)*
>
> ?time n.risk n.event survival std.err lower 95% CI upper 95% CI
> ? 59 ? ? 26 ? ? ? 1 ? ?*0.978* ?0.0240 ? ? ? ?0.932 ? ? ? ?1.000
> ?115 ? ? 25 ? ? ? 1 ? ?*0.952* ?0.0390 ? ? ? ?0.878 ? ? ? ?1.000
> ?156 ? ? 24 ? ? ? 1 ? ?*0.917* ?0.0556 ? ? ? ?0.814 ? ? ? ?1.000
> ?268 ? ? 23 ? ? ? 1 ? ?*0.880* ?0.0704 ? ? ? ?0.752 ? ? ? ?1.000
> ?329 ? ? 22 ? ? ? 1 ? ?*0.818* ?0.0884 ? ? ? ?0.662 ? ? ? ?1.000
> ?353 ? ? 21 ? ? ? 1 ? ?*0.760* ?0.0991 ? ? ? ?0.588 ? ? ? ?0.981
> ?365 ? ? 20 ? ? ? 1 ? ?*0.698* ?0.1079 ? ? ? ?0.516 ? ? ? ?0.945
> ?431 ? ? 17 ? ? ? 1 ? ?*0.623* ?0.1187 ? ? ? ?0.429 ? ? ? ?0.905
> ?464 ? ? 15 ? ? ? 1 ? ?*0.549* ?0.1248 ? ? ? ?0.352 ? ? ? ?0.858
> ?475 ? ? 14 ? ? ? 1 ? ?*0.480* ?0.1267 ? ? ? ?0.286 ? ? ? ?0.805
> ?563 ? ? 12 ? ? ? 1 ? ?*0.382* ?0.1332 ? ? ? ?0.193 ? ? ? ?0.757
> ?638 ? ? 11 ? ? ? 1 ? ?*0.297* ?0.1292 ? ? ? ?0.127 ? ? ? ?0.697
>
> ? ? ? ?[[alternative HTML version deleted]]
>
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-- 
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

tel : +32 9 264 59 87
Joris.Meys at Ugent.be
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