Maybe the title is redundant -- I'm not sure how many standard-bearer texts exist on robust biostatistics exist (Huber's general treatment of robust statistics, in its revised edition, is quite good, but it does not cover some of the practicalities involved in longitudinal studies or survival analysis). This one has a full development of the relevant M-estimators and R-estimators without a ton of measure theoretic filler (sorry folks, but if measure theory was relevant to me, my committee would have forced me to take it). My copy -- actually, the University's copy -- currently resides with a physicist friend who signed on to work with me on a doubly robust model for estimating side effect risks. I didn't realize it until recently, but this book also covers some of the material underlying marginal structural models, in addition to a good treatment of standard, weighted, and robust GLM practicalities. The book contains some example code in R, but it is most certainly not an 'R book' -- do not expect a hand-holding practicum on how to use someone else's packages, because that is not what the book is about.

I haven't seen a better treatment of the material. It's not McCullagh & Nelder, but it's as well written as Huber's book, and that's no small feat in itself.

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