The second half of JSM was just as eventful as the first half. Jim Goodnight addressed the new practical problems requiring analytics. Perhaps telling, though is his almost begrudging admission that R is big. The reality is that SAS seems to think they are going to have to work with R in the future. There is already some integration in SAS/IML studio, and I think that is going to get tighter.
The evening brought a couple of reunions and business meetings, including the UNC reunion (where it sounds like my alma mater had a pretty good year in terms of faculty and student awards and contributions) and the statistical computing and graphics sections, where I met some of my fellow tweeters.
On Tuesday, I went a little out of my normal route and attended a session on functional data analysis. This is one area I think we biostatisticans could use more ideas. Ramsay (who helped create and advance the field) discussed software needs for the field (with a few interesting critques of R), and two others talked about two interesting applications to biostatistics, including studying cell apoptosis and brain imaging study of lead exposure. On Wednesday afternoon, we discussed patient population segmentation and tailored therapeutics, which is I guess an intermediate step between marketing a drug to everybody and personalized medicine. I think everybody agreed that personalized medicine is the direction we are going, but we are going to take a long time to get there. Patient segmentation is happening today. Tuesday night brought Revolution Analytics's big announcement about their commercial big data package for R, where you can analyze 100 million row datasets in less than a minute on a relatively cheap laptop. I saw a demo of the system, and they even tried to respect many of the conventions in R, including the use of generic functions. Thanks to them for the beeR, as well. Later on in the evening brought more business meetings. I ended up volunteering for some work for next year, and I begin next week.
On Wednesday, I attended talks on missing data, vaccine trials and practical issues in implementing adpative trials. By then, I was conferenced out, having attended probably 10 sessions over 4 days, for a total of 20 hours absorbing ideas. And that didn't include the business part.
I will present some reflections on the conference, including issues that will either emerge or continue to be important in statistical analysis of clinical trials.
