I was recently reading a bit about logistic regression in Gelman and Hill's book on hierarchical/multilevel modeling when I first learned about the "divide by 4 rule" for quickly interpreting coefficients in a logistic regression model in terms of the predicted probabilities of the outcome. The idea is pretty simple.

I've had friends in biochem "wet" labs who've asked me to do some simple statistics on some of their results. This looks like an interesting seminar to attend if you've ever thought about doing a t-test on fold changes in some outcome measure between treatment and control groups, a pretty common outcome in biochemical assays. If the speaker provides slides electronically I'll happily post them here after the seminar.

Found this tutorial by Emily Mankin on how to do principal components analysis (PCA) using R. Has a nice example with R code and several good references. The example starts by doing the PCA manually, then uses R's built in prcomp() function to do the same PCA. Principle Components Analysis: A How-To Manual for R Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Several readers emailed me or left a comment on my previous announcement of Frank Harrell's workshop on using Sweave for reproducible research asking if we could record the seminar. Unfortunately we couldn't record audio or video, but take a look at the Sweave/Latex page on the Biostatistics Dept Wiki.

If you attended Frank Harrell's Regression Modeling Strategies course a few weeks ago, you got a chance to see the rms package for R in action. Frank's rms package does regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit.

Update 2010-04-21 : I forgot to post the link last time. That would have been helpful. Here you go: Vanderbilt Biostatistics: Statistical Problems to Document and to Avoid ..... At the Regression Modeling Strategies course I attended a few weeks ago, Frank Harrell pointed out the checklist on the biostatistics department's website of statistical problems to document and avoid.

This month's cancer biostatistics workshop on overfitting will be given by Fei Ye and Zhiguo (Alex) Zhao, both in the Department of Biostatistics and the Cancer Biostatistics Center. This looks like a good one, especially after attending Frank Harrell's regression modeling strategies course a few weeks ago. See the link below for the full 2010 series.

I recently did an analysis for the eMERGE network where I had lots of individuals from a small town in central Wisconsin where many of the subjects were related to one another. The subjects could not be treated as independent, but I could not use a family-based design either. I ended up using a mixed model approach using previously mentioned GenABEL.

I've been using GenABEL for some time now for GWAS analysis using related individuals. It has an excellent set of functions for estimating a kinship matrix from a dense marker panel and then using this in a linear mixed effects model to allow for related individuals in the analysis of a quantitative trait.

The previously mentioned Regression Modeling Strategies short course taught by Frank Harrell is nearly over. Here are the handouts (PDF) from the course. Keep an eye out here, I'll be writing a few more posts in the near future on topics Frank covered in this course.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.