--- 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. ______________________________________________ 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.
