On 07/26/2013 04:06 AM, John Sorkin wrote:
David Thank you for your thoughts. The data I am analyzing do not
come from a clinical trial but rather from a cohort study whose aim
is to determine risk factors for surgical therapy to treat their
joints. John
As David explained, there are several ways of approaching this
situation. If it is a treatment/control case (which yours isn't),
stratification is appropriate. It boils down to a simple sign test
where we compare the number of pairs with longest survival of the
treated with the number of pairs with the longest survival of the
control. Undetermined (due to censoring) pairs are thrown away.
Generally, stratification is an alternative to the frailty model, but it
has some drawbacks: loss of power (especially with small stratum sizes),
and you cannot use covariates that are constant within pairs (personal
characteristics in your case). The frailty model comes with stronger
assumptions than stratification, but you avoid the drawbacks just
mentioned. The clustering method, finally, is for variance correction in
the ordinary Cox regression.
In your case, would recommend the frailty approach with coxme (while we
wait for Terry's verdict!).
Göran
Sent from my iPhone
On Jul 25, 2013, at 9:15 PM, "Marc Schwartz <marc_schwa...@me.com>"
<marc_schwa...@me.com> wrote:
On Jul 25, 2013, at 4:45 PM, David Winsemius
<dwinsem...@comcast.net> wrote:
On Jul 25, 2013, at 12:27 PM, Marc Schwartz wrote:
On Jul 25, 2013, at 2:11 PM, John Sorkin
<jsor...@grecc.umaryland.edu> wrote:
Colleagues, Is there any R package that will allow one to
perform a repeated measures Cox Proportional Hazards
regression? I don't think coxph is set up to handle this type
of problem, but I would be happy to know that I am not
correct. I am doing a study of time to hip joint replacement.
As each person has two hips, a given person can appear in the
dataset twice, once for the left hip and once for the right
hip, and I need to account for the correlation of data from a
single individual. Thank you, John
John,
See Terry's 'coxme' package:
http://cran.r-project.org/web/packages/coxme/index.html
When I looked over the description of coxme, I was concerned it
was not really designed with this in mind. Looking at Therneau
and Grambsch, I thought section 8.4.2 in the 'Multiple Events per
Subject' Chapter fit the analysis question well. There they
compared the use of coxph( ...+cluster(ID),,...) withcoxph(
...+strata(ID),,...). Unfortunately I could not tell for sure
which one was being described as superio but I think it was the
cluster() alternative. I seem to remember there are discussions
in the archives.
David,
I think that you raise a good point. The example in the book (I had
to wait to get home to read it) is potentially different however,
in that the subject's eye's were randomized to treatment or
control, which would seem to suggest comparable baseline
characteristics for each pair of eyes, as well as an active
intervention on one side where a difference in treatment effect
between each eye is being analyzed.
It is not clear from John's description above if there is one hip
that will be treated versus one as a control and whether the extent
of disease at baseline is similar in each pair of hips. Presumably
the timing of hip replacements will be staggered at some level,
even if there is comparable disease, simply due to post-op recovery
time and surgical risk. In cases where the disease between each hip
is materially different, that would be another factor to consider,
however I would defer to orthopaedic physicians/surgeons from a
subject matter expertise consideration. It is possible that the
bilateral hip replacement data might be more of a parallel to
bilateral breast cancer data, if each breast were to be tracked
separately.
I have cc'd Terry here, hoping that he might jump in and offer some
insights into the pros/cons of using coxme versus coxph with either
a cluster or strata based approach, or perhaps even a frailty based
approach as in 9.4.1 in the book.
Regards,
Marc
-- David.
You also might find the following of interest:
http://bjo.bmj.com/content/71/9/645.full.pdf
http://www.ncbi.nlm.nih.gov/pubmed/22226885
http://www.ncbi.nlm.nih.gov/pubmed/22078901
Regards,
Marc Schwartz
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David Winsemius Alameda, CA, USA
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