[R] Kaplan-Meier plotting quirks
Michael Rentz
rent0009 at umn.edu
Fri Oct 19 17:35:16 CEST 2012
Terry: Thank you, that makes quite a bit of sense. In transposing the data
to intervals (corresponding to trapping runs) it becomes quite clear that
tag loss is very high up front, and has very good survival after the
initial period. This is what I needed to know. I think I had a case of too
many trees to see the forest. I did not set up exactly as you suggest, but
since I do not expect there to be any interval effect (just number of
intervals) I think I am OK.
Thank you again, and glad to see another Minnesotan here.
Mike.
On Oct 18 2012, Terry Therneau wrote:
Better would be to use interval censored data. Create your data set so
that you have
(time1, time2) pairs, each of which describes the interval of time over
which the tag was
lost. So an animal first captured at time 10 sans tag would be (0,10);
with tag at 5 and
without at 20 would be (5,20), and last seen with tag at 30 would be (30,
NA).
Then survit(Surv(time1, time2, type='interval2') ~ 1, data=yourdata) will
give a curve
>that accounts for interval censoring.
As a prior poster suggested, if the times are very sparse then you may
be better off
assuming a smooth curve. Use the survreg function with the same equation
as above; see
help("predict.survreg") for an example of how to draw the resulting
survival curve.
>
>Terry Therneau
>
>On 10/18/2012 05:00 AM, r-help-request at r-project.org wrote:
>> -----Original Message-----
>> From: Michael Rentz [mailto:rent0009 at umn.edu]
>> Sent: Tuesday, October 16, 2012 12:36 PM
>> To:r-help at r-project.org
>> Subject: [R] R Kaplan-Meier plotting quirks?
>>
Hello. I apologize in advance for the VERY lengthy e-mail. I endeavor to
include enough detail.
>>
I have a question about survival curves I have been battling off and on
for a few months. No one local seems to be able to help, so I turn here.
The issue seems to either be how R calculates Kaplan-Meier Plots, or
something with the underlying statistic itself that I am misunderstanding.
Basically, longer survival times are yielding steeper drops in survival
than a set of shorter survival times but with the same number of loss and
retention events.
>>
As a minor part of my research I have been comparing tag survival in
marked wild rodents. I am comparing a standard ear tag with a relatively
new technique. The newer tag clearly ?wins? using survival tests, but the
resultant Kaplan-Meier plot does not seem to make sense. Since I am dealing
with a wild animal and only trapped a few days out of a month the data is
fairly messy, with gaps in capture history that require assumptions of tag
survival. An animal that is tagged and recaptured 2 days later with a tag
and 30 days later without one could have an assumed tag retention of 2 days
(minimum confirmed) or 30 days (maximum possible).
>>
Both are significant with a survtest, but the K-M plots differ. A plot
of minimum confirmed (overall harsher data, lots of 0 days and 1 or 2 days)
yields a curve with a steep initial drop in ?survival?, but then a leveling
off and straight line thereafter at about 80% survival. Plotting the
maximum possible dates (same number of losses/retention, but retention
times are longer, the length to the next capture without a tag, typically
25-30 days or more) does not show as steep of a drop in the first few
days, but at about the point the minimum estimate levels off this one
begins dropping steeply. 400 days out the plot with minimum possible
estimates has tag survival of about 80%, whereas the plot with the same
loss rate but longer assumed survival times shows only a 20% assumed
survival at 400 days. Complicating this of course is the fact that the
great majority of the animals die before the tag is lost, survival of the
rodents is on the order of months.
>>
I really am not sure what is going on, unless somehow the high number of
events in the first few days followed by few events thereafter leads to the
assumption that after the initial few days survival of the tag is high. The
plotting of maximum lengths has a more even distribution of events, rather
than a clumping in the first few days, so I guess the model assumes
relatively constant hazards? As an aside, a plot of the mean between the
minimum and maximum almost mirrors the maximum plot. Adding five days to
the minimum when the minimum plus 5 is less than the maximum returns a plot
with a steeper initial drop, but then constant thereafter, mimicking the
minimum plot, but at a lower final survival rate.
>>
Basically, I am at a loss why surviving longer would*decrease* the
survival rate???
>>
My co-author wants to drop the K-M graph given the confusion, but I
think it would be odd to publish a survival paper without one. I am not
sure which graph to use? They say very different things, while the actual
statistics do not differ that greatly.
>>
I am more than happy to provide the data and code for anyone who would
like to help if the above is not explanation enough. Thank you in advance.
>>
>> Mike.
>>
>>
>> --
>> Michael S. Rentz
>> PhD Candidate, Conservation Biology
>> University of Minnesota
>> 5122 Idlewild Street
>> Duluth, MN 55804
>> (218) 525-3299
>> rent0009 at umn.edu
>
--
Michael S. Rentz
PhD Candidate, Conservation Biology
University of Minnesota
5122 Idlewild Street
Duluth, MN 55804
(218) 525-3299
rent0009 at umn.edu
More information about the R-help
mailing list