A.S.: I am sorry for sending my first mail to <r-help at R-
project.org>.
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Hallo,
computing a Cox proportional hazards model in SPSS 9.0 and
R 1.2.2 produces different results for beta-coefficient.
I use the follwing data set (source: example in
help(coxph), somewhat modified)
Time Status Covariate (x)
-------------------------
4,00 1,00 ,00
3,00 1,00 1,00
1,00 1,00 1,00
1,00 ,00 1,00
2,00 1,00 1,00
2,00 1,00 ,00
3,00 ,00 ,00
The results in SPSS:
--------------------
beta = 1,44
exp(beta) = 4,20
SE = 1,17
SPSS Syntax:
--------------------
COXREG
t /STATUS=s(1)
/METHOD=ENTER x
/CRITERIA=PIN(.05) POUT(.10) ITERATE(20) .
The results in R:
--------------------
beta = 1.46
exp(beta) = 4,32
SE = 1,17
R Syntax:
--------------------
test1 <- list(time= c(4, 3,1,1,2,2,3),
status=c(1,1,1,0,1,1,0),
x= c(0, 1,1,1,1,0,0),
summary(coxph( Surv(time, status) ~ x, test1))
BTW: The results of SPSS and TDA (Transition Data Analysis)
correspondent exactly.
What went wrong? I strongly suppose, I did some mistakes,
but I can't image what kind of mistakes. Any ideas?
Thanks in advance
Bernd
--
Bernd Wei? (bernd.weiss at epost.de)
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On Sun, 11 Mar 2001, Bernd Weiss wrote:> computing a Cox proportional hazards model in SPSS 9.0 and > R 1.2.2 produces different results for beta-coefficient.Your data have ties in the times. The original theory for Cox models applies to continuous hazard distributions, for which there can be no ties. There are various fixes, controlled by the argument of coxph: method: a character string specifying the method for tie handling. If there are no tied death times all the methods are equivalent. Nearly all Cox regression programs use the Breslow method by default, but not this one. The Efron approximation is used as the default here, as it is much more accurate when dealing with tied death times, and is as efficient computationally. The exact method computes the exact partial likelihood, which is equivalent to a conditional logistic model. If there are a large number of ties the computational time will be excessive. I think that para (from ?coxph) tells you all you need to know, except that `accurate' means by reference to the `exact' method. [...] -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272860 (secr) Oxford OX1 3TG, UK Fax: +44 1865 272595 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
On 11 Mar 2001, at 7:51, Prof Brian D Ripley wrote:> On Sun, 11 Mar 2001, Bernd Weiss wrote: > > > computing a Cox proportional hazards model in SPSS 9.0 and > > R 1.2.2 produces different results for beta-coefficient. >[...]> > method: a character string specifying the method for tie > handling. > If there are no tied death times all the methods > are equivalent. Nearly all Cox regression programs > use the Breslow method by default, but not this > one. The Efron approximation is used as the > default here, as it is much more accurate when > dealing with tied death times, and is as efficient > computationally. The exact method computes the > exact partial likelihood, which is equivalent to a > conditional logistic model. If there are a large > number of ties the computational time will be > excessive. >[...] <coxph(Surv(t,s)~x, method="breslow", data=coxdata)> works fine. Thanks for your help. Bernd -- Bernd Wei? (bernd.weiss at epost.de) PGP Key ID: 0x4117206F PGP FP: 08B2 09CD 7192 526D 93FD 2070 53DB 7C4F 4117 206F www.pgpi.org -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._