On May 13, 2011, at 8:49 AM, Terry Therneau wrote:
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Is there an automated way to use the survival package to generate
survival
rate estimates and their standard errors? To be clear, *not *the
survivorship estimates (which are cumulative), but the survival
*rate *
estimates...
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The classic method in epidemiology is simple hazard rates = total
events / total time. This is done in more general form by the pyears
(person-years) routine.
pfit <- pyears(Surv(time, status) ~ agegrp + sex, data=mydata)
It's advantage over simple tables is the ability to do time-dependent
categories. For instance if I want death rates by decade of age:
acut <- tcut(mydata$age, 0:12 *10)
pfit2 <- pyears(time, status) ~ acut + sex, data=mydata)
I think there was an omitted Surv function call above.
The first table was based on age at the start, the second on current
age.
I didn't appreciate that comment in my first reading. Thnaks to Brian
for asking and Terry for authorship. That will make a big difference
for my work. So now the first question is: How does 'acut' get handled
internally? It looks like a factor, but I would have guessed that in
order to deal with decade-crossing survival "non-events", that it
would need to create some multiple records. It doesn't seem to do
this, so perhaps something like that happen in the pyears function.
And a follow on question and a tentative answer: Can one generate a
'pyears' table that incorporates categories of time observed by tcut()-
ting the time variable and having it in both the Surv argument and on
the RHS of the formula? Experimentation makes me think this works very
well.
ddf <- data.frame(id = 1:100, surv.year=runif(100, 0,10),
death=sample(c(0,1), 100, prob=c(0.7, 0.3), replace=TRUE) )
pfit2$event/pfit2$pyears
#----
0+ thru 2 2+ thru 4 4+ thru 6 6+ thru 8 8+ thru 10
105.506642 80.474685 16.271176 20.165972 7.896898
For non-parametric estimates of the hazard function look at the
"survival" task view on CRAN; there are several choices.
Terry Therneau
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David Winsemius, MD
West Hartford, CT
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