Beyond the basics: intermediate medical statistics textbooks suggestions I am a soon-to-be physician. During my studies I have taken a class in biostatistics. I own Martin Bland's "An introduction to medical statistics", which was a required textbooks at the time, and Harvey Motulsky's "Intuitive biostatistics", which I've purchased on my own initiative based on positive reviews on Amazon. They served me well, but I'm interested in clinical research and I feel that I need to stop scratching the surface and delve deeper to understand statistics.
I'm looking for one or more textbooks I can use to self study. Ideally the textbook(s) should go beyond the basics, and explain modern statistical techniques used in biomedical research (e.g reading these forums I've seen a lot of mentions of Bayesian statistics, which seems to be the Next Big Thing). If possible, it should feature examples, and not assume a particular statistical package (unless it is R, that is the software I use).
I should probably mentions that my math skills are ... rusty. So feel free to list the kind of math skills required to understand the book(s) you are suggesting.
 A: I'd strongly suggest you get Frank Harrell's Regression Modeling Strategies. 
Possibly not as your only book - you'll might want something more elementary as well (though since you've already read some books, this may be covered) - but this book is full of practical and important information, and if you use R (which is also an important tool, I think), then it's doubly valuable --- but highly valuable whether you use it or not.
I am not in the medical area, but nevertheless I tell all my prospective research students (if they haven't already read it) to borrow it (and consider buying it). I insist that at the very least they read chapter 4, which I think is unmissable information for anyone using regression - since all my subsequent discussion with them will assume they know what it says there.
A: Here are three recommendations of books that focus on plain language explanations and practical suggestions for conventional (not Bayesian or computer intensive) methods but also include the mathematical details:
 Maxwell and Delaney
 Machin
 Glantz and Slinker
A: *

*Vittinghoff. Regression Models in Biostatistics (There is a Stata emphasis that might be annoying. In addition the Second edition isn't as well written as the first.)

*Steyerberg. Prediction Modelling

*? Frank Harrell. Regression Modeling Strategies. I've never read so cannot vouch for. Seems like a natural choice if you're using R. Bit pricey.  

*do research. less statistics involved than it seems.  

