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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).
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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.