Two choices.
If this were a linear model, do you like the GEE approach or a mixed effects approach?
Assume that "subject" is a variable containing a per-subject identifier.
GEE approach: add "+ cluster(subject)" to the model statement in coxph
Mixed models approach: Add " + (1|subject)" to the model statment in coxme.
When only a very few subjects have multiple events, the mixed model (random effect)
approach may not be reliable, however. Multiple events per group are the fuel for
estimation of the variance of the random effect, and with few of these the profile
likelihood of the random effect will be very flat. You can get esssentially a random
estimate of the variance of the "subject effect". I'm still getting my arms around this
issue, and it has taken me a long time.
"Frailty" is an alternate label for "random effects when all we have is a random
intercept". Multiple labels for the same idea adds confusion, but nothing else.
Terry Therneau
On 07/25/2013 08:14 PM, Marc Schwartz 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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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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and provide commented, minimal, self-contained, reproducible code.
