Repeated measures Cox regression ??coxph??

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<dwinsemius at comcast.net>  wrote:

On Jul 25, 2013, at 12:27 PM, Marc Schwartz wrote:

On Jul 25, 2013, at 2:11 PM, John Sorkin<jsorkin at 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

David Winsemius
Alameda, CA, USA