Best reference for using confidence intervals in lieu of p-values I am working on a high profile manuscript which will go into a journal oriented on translational or medical science. In the paper, several ROC curves have been presented to show the performance of machine learning models in detecting a particular disease. As estimates of the performance, I have given AUC values and confidence intervals; p-values have been added to a supplementary table.
I now have been criticized for "unconventional" presentation and "using latest guidelines", which was, for me, surprising. I thought that showing CI's when estimating was the convention and has been for a while (decades?) now, and many better and more "unconventional" replacements for CI have been proposed.
The argument is, we should use the p-values, because that is what the reviewers expect. I think that this is incorrect, since we are showing estimates, rather than results of planned comparisons. What would you recommend? If CI, than what recommendations / papers should I present in favor of this view?
 A: Reviewers are just being cranky and wanting to see what they have always seen. Science is always evolving and changing; this makes people uncomfortable. What you have presented is "unconventional" in a sense that it does not use methods developed in the early 1900s that—for some reason—people still use. It is not "unconventional" to anyone who has read a statistics book published in the last 5 years (which will certainly be very view reviewers). But enough of my rant...
I am in psychology, where there has been a push against p-values for a long time, but it has recently picked up more steam. If you want people praising confidence intervals, Geoff Cumming does that in this highly-cited paper as well as in his book. The Term "New Statistics" is misleading, since it still relies on the frequentist perspective and has been used for decades. I would check out people who cite him and talk about confidence intervals; Cumming is probably the biggest proponent of effect sizes and confidence intervals I've read.
I am not at my computer, but John Ioannidis also has some work on how p-values are very fickle. 
And, as always, you have the Bayesians that will put down p-values, but since you are still operating under a frequentist paradigm (i.e., using confidence intervals), it probably isn't appropriate to cite them.
