Sorry I'm late with this.
On 07/26/2013 02:02 PM, Terry Therneau wrote:
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.
Note that the 'cluster' approach ignores the clustering regarding the
regression parameter estimates. It tries to correct the optimistic
variance estimate given by ignoring the clustering, but it does nothing
about the bias that may be introduced.
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.
John had exactly two observations per subject, and given that a frailty
model is reasonable, the bias may be substantial if ignoring it. I made
a small simulation study to convince myself: frailty variance = 1, one
binary covariate (constant within subjects) and beta coefficient = 1.
With 20 subjects, the bias for coxme was -0.004, for coxph (with
'cluster', but it doesn't matter) -0.294 (based on 1000 replicates).
(The bias for the frailty standard deviation was -0.108, but who cares
when we regard it as just a nuisance?)
Of course this doesn't prove anything, but it makes me worried; it is
easy to understand the frailty model, but what is the 'GEE' model in
this survival case? Why should it be used in John's case?
"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.
The term "frailty" was (to my knowledge) coined by Vaupel, Manton &
Stallard in a 1979 paper in 'Demography'. They used it to describe
heterogeneity in demographic data, and what could happen if it was
ignored. Just for the record.
Göran
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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______________________________________________ R-help@r-project.org
mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do
read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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