R help - Jul 2002 - Questions concerning survival analysis

Good morning everyone (or maybe good evening)

Is there a counterpart to the s-plus function "probplot" (which
provides a qq-plot for "survreg"-objects)? Or do exist other
(rather simple) possibilities to check the assumptions of the
distribution?

I have another question to the author(s) of summary.survreg:
Why does summary(...,times=sort(x)) not give the same result as
summary(...,censored=TRUE) ? Example:

###
library(survival)

x <-   c(1,1,2,4,4,4,6,9)
status <- c(1,0,1,1,1,0,1,1) 

r.sf <- survfit(Surv(x, status)~1,
type='kaplan-meier',conf.type="none")

summary(r.sf,times=sort(x))
summary(r.sf,censored=TRUE) # the censored data are not listed (?)
###

regards,
Ruth Meili

-- 
Ruth Meili <meili at stat.math.ethz.ch>
Seminar fuer Statistik, LEO D6
ETH (Federal Inst. Technology)	8092 Zurich	 SWITZERLAND
phone: x-41-1-632-4684		fax: x-41-1-632-1228
http://stat.ethz.ch/~meili/
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On Tue, 30 Jul 2002, Ruth Meili wrote:
> I have another question to the author(s) of summary.survreg:
> Why does summary(...,times=sort(x)) not give the same result as
> summary(...,censored=TRUE) ? Example:
>
> ###
> library(survival)
>
> x <-   c(1,1,2,4,4,4,6,9)
> status <- c(1,0,1,1,1,0,1,1)
>
> r.sf <- survfit(Surv(x, status)~1,
type='kaplan-meier',conf.type="none")
>
> summary(r.sf,times=sort(x))
> summary(r.sf,censored=TRUE) # the censored data are not listed (?)
> ###
Well, the author of summary.survreg doesn't read r-help,  but in any case
censored=TRUE works as documented

    			If `censored=T', the default `times'
    vector contains all the unique times in `fit', otherwise the
    default `times' vector uses only the event (death) times.
> summary(r.sf,censored=TRUE) # the censored data are not listed (?)
Call: survfit(formula = Surv(x, status) ~ 1, type = "kaplan-meier",
    conf.type = "none")

 time n.risk n.event survival std.err
    1      8       1    0.875   0.117
    2      6       1    0.729   0.165
    4      5       2    0.438   0.188
    6      2       1    0.219   0.181
    9      1       1    0.000      NA


Every observed time point is listed, regardless of whether an event
occurred.  In this example an event occured at every time point so
specifying censored=TRUE has no effect

On the other hand
> summary(r.sf,times=sort(x))
Call: survfit(formula = Surv(x, status) ~ 1, type = "kaplan-meier",
    conf.type = "none")

 time n.risk n.event survival std.err
    1      8       1    0.875   0.117
    1      8       0    0.875   0.117
    2      6       1    0.729   0.165
    4      5       2    0.438   0.188
    4      5       0    0.438   0.188
    4      5       0    0.438   0.188
    6      2       1    0.219   0.181
    9      1       1    0.000      NA

does not give the right answer because there are repeated times. Note for
example the n.event column for time 4.  The help page says that the times
must be in increasing order.  In fact they must be strictly increasing --
repeats are not allowed.


	-thomas

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Hello,

I would like to know how to test whether the 'subject' random effect is 
significant in a generalized linear model.

I am using glmmPQL() in the MASS package.
The response is binary and my model only has an intercept as fixed effect 
and 'subject' as random effect . That is, the fit is:

glmmPQL(y ~ 1 , random = ~ 1 | subject , family=binomial , data=data)

Thanks in advance
Daniel





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