I'm curious. I've used the paired-prentice Wilcoxon test for the analysis 
of parried survival data. I haven't run into use of the coxph for that 
previously, but I have seen it referenced a couple of times in recent web 
searches.

I have a data set of subjects like this:

Subject    T1  R1   T2 R2
      1    32   >   31
      2    28       27
      3    30       31
...

where Subject is the id of the subject, T1 is the test result from test 1, 
R1 is the remark code (> indicates that the test result is the lower end, 
right censoring only) and T2 and R2 are the corresponding values for test 
2. I would like to know if there is a change from test 1 to test 2.

How can I set up the call to coxph for the paired test?
Thanks much.
Dave



From:
Terry Therneau <therneau@mayo.edu>
To:
"Marcus Michelangeli (Sci)" <marcus.michelangeli@monash.edu>
Cc:
r-help@r-project.org
Date:
01/25/2011 09:42 AM
Subject:
Re: [R] Paired data survival analysis
Sent by:
r-help-bounces@r-project.org



--- begin included message ---

Im an honours student at Monash University. I'm trying to analyse some
data for my project, which involved 2 treatments. My subjects were
exposed to both treatments, and i gave them 60 minutes to perform a
certain behaviour.  3 of my subjects performed the behaviour in one
treatment but not the other. Therefore, i need to do a survival
analysis using paired data. Im little confused about how to go about
this in R. Im able to perfrom a normal surival analyses not taking the
paired data into account, but im just wondering if there is some way
to take the pairing into account. I know there are 3 different ways to
deal with grouping in the survival package, strata, cluster and
frailty but i struggle to understand the meaning of these arguments
and therefore do not know which one to use (if any).

--- end inclusion ---

All 3 methods can be defended.  Adding cluster(id) to the model is
equivalent to a generalized estimating equations approach (if this were
a glm) or to the variance estimates commonly used in survey sampling (if
this were a linear model).  Adding frailty(id) is equivalent to fitting
a linear mixed model.  Using strata corresponds to a matched-pair
analysis, and will essentially reduce to a sign test: for each subject
treatment A was better, B was better, or tied.  It's overkill in this
case (lower power).
   If this were a linear model, you could find strong advocates for
either the GEE and mixed approach being "better".  I somewhat prefer the
GEE method myself. 

Terry T.

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