I really appreciate your advice. I am a beginner in survival
analysis, in fact, in S/R.
Maybe i should clarify what I would like to do.
I would like to look to plot both the instant hazard rate of a
biomarker positive and negative expression respectively, and also the
hazard ratio of the positive in respect to the negative expression,
adjusting with other factors, such as tumor size and age.
I read this reference paper (Estrogen receptors and distinct patterns
of breast cancer relapse, by Kenneth R. Hess et al, Breast cancer
research and treatment 78: 105-118, 2003). In the paper, the authors
plot the a smooth hazard graph, and also hazard ratio.
I am not quite sure how the author draw the smooth hazard ratio, but
I like the idea that plotting the hazard ratio across time, to
visualize the changes.
Thanks in advance.
Maggie
On 28-Sep-05, at 1:18 AM, Prof Brian Ripley wrote:
On Tue, 27 Sep 2005, Maggie Cheang wrote:
Thanks for Terry to reply for my qustion.
Note that it is implicit in Terry's answer that the hazard is smooth.
You have to make some asssumption here, and his includes how smooth
it is.
That comes to my second question,
is there a simple way to plot the Hazard ratio from a coxph?
What hazard ratio is that? Note the theory underlying coxph does
not assume that hazards are smooth but that they are very rough.
anyone familiar with the muhaz package?
Yes, and it is discussed (alongside similar ideas) in the MASS on-
line complements. See http://www.stats.ox.ac.uk/pub/MASS4/
#Complements .
thanks a bunch
Maggie
On Tuesday, September 27, 2005, at 06:24AM, Terry Therneau
<therneau@mayo.edu> wrote:
The simplest way that I know to get an estimate of the
hazard is to use smoothing splines.
fit <- survfit(Surv(time, status) ~ sex, data=lung)
temp1 <- smooth.spline(fit[1]$time, 1-fit[1]$surv, df=5)
temp2 <- smooth.spline(fit[2]$time, 1-fit[2]$surv, df=5)
plot( predict(temp1, deriv=1), type='l')
lines(predict(temp2, deriv=1), col=2)
------
The lung cancer data set is part of Splus (it's used in several
manual pages as an example). The plot shows the males to have a
higher initial hazard than the females.
You choice of 5 degrees of freedom for the spline was completely
arbitrary.
Terry Therneau
--------------------------------------------------------------------
This message was distributed by s-news@lists.biostat.wustl.edu. To
unsubscribe send e-mail to s-news-request@lists.biostat.wustl.edu
with
the BODY of the message: unsubscribe s-news
--------------------------------------------------------------------
This message was distributed by s-news@lists.biostat.wustl.edu. To
unsubscribe send e-mail to s-news-request@lists.biostat.wustl.edu
with
the BODY of the message: unsubscribe s-news
--
Brian D. Ripley, ripley@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 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
--------------------------------------------------------------------
This message was distributed by s-news@lists.biostat.wustl.edu. To
unsubscribe send e-mail to s-news-request@lists.biostat.wustl.edu with
the BODY of the message: unsubscribe s-news
Maggie Cheang
mcheang@mac.com
|