I’m starting a new project as an independent consultant. Normally, I talk very little on my blog about specific projects that I work for, but this work is for a group, the Great Plains Collaborative (GPC) that is open about almost every aspect of the work they do. That’s music to my ears. Anyway, the GPC is involved with several projects and one of the ones I might do some of my work on is the Great Plains Collaborative Breast Cancer Study. Here is some information about this study, culled from sources available to anyone on the Internet. Continue reading
A flip of a coin does not result in an exact 50-50 chance of heads or tails. It depends a lot on how the coin is flipped, of course, but there is a bias. This article explains when, why, and how much bias there is. Continue reading
When I wrote a book about Evidence Based Medicine back in 2006, I talked about empirical evidence to support the use of certain research methodologies like blinding and allocation concealment. Since that time, many more studies have appeared, more than you or I could easily keep track of. Thankfully, the folks at the Agency for Healthcare Research and Quality commissioned a report to look at studies that empirically evaluate the bias reduction of several popular approaches used in randomized trials. These include
selection bias through randomization (sequence generation and allocation concealment); confounding through design or analysis; performance bias through fidelity to the protocol, avoidance of unintended interventions, patient or caregiver blinding and clinician or provider blinding; detection bias through outcome assessor and data analyst blinding and appropriate statistical methods; detection/performance bias through double blinding; attrition bias through intention-to-treat analysis or other approaches to accounting for dropouts; and reporting bias through complete reporting of all prespecified outcomes.
The general finding was that failure to use these bias reduction approaches tended to exaggerate treatment effects, but the magnitude and precision of these exaggerated effects was inconsistent. Continue reading
I don’t have time to follow the journals as closely as I should, but I was shocked to find two very nasty editorials in recent issues of the New England Journal of Medicine. They are sharply critical of open sharing of data and of quality improvement efforts. Continue reading
I got an email from someone at UMKC with the title, Director of Undergraduate Research. She was
“looking through the abstract booklet from the Faculty Research Symposium sponsored by Lawrence Dreyfus’s office at the end of last semester, and I was really intrigued by your presentation on the likelihood ration slide rule. That’s just the kind of innovative work that undergraduate students would like to be involved with, and you would be an awesome mentor for undergraduate researchers.”
Flattery always works with me, so I took her suggestion of setting a faculty profile that undergraduate students at UMKC could review. Here’s what I put on that profile. Continue reading
Dear Professor Mean, I have a data set where 94 out of 100 patients with cancer have activation of a gene, while 0 out of 50 of the controls have activation. When I compute the odds ratio, I get (94*50) / (6*0) = 4700 / 0 = ???. What should I do? Continue reading
The genetics research community should be lauded for the openness with which they share research data. You can find numerous data sources that are free and without ANY restrictions. One very good example is ENCODE, the Encyclopedia of DNA Elements. This repository, mostly of human data, but some mouse, fruit fly, and round worm data as well. It has data from many different assays including ChIP-seq, RNA-seq, and DNase-seq. It looks like a great teaching resource, though it does require a fairly hefty understanding of genetics to browse through the data. Continue reading
I was asked by a colleague to write a chapter for a book he was editing, Big Data Analysis for Bioinformatics and Biomedical Discoveries. My chapter was “R for Big Data Analysis.” It just about killed me to write that chapter, but I got it done about nine months ago, and now the book is out officially. Continue reading
I get a lot of emails mentioning job openings and I normally delete them unread. This one caught my eye, not because I wanted to apply for it, but because it illustrates how statisticians get to work on very interesting jobs. This is a Senior Statistician job at the Lyndon B. Johnson Space Center in Houston. If you got this job, you’d be providing assistance on “a wide range of biomedical and technical areas in support of space exploration.” How cool is that!
The other interesting thing is that they say that “accreditation by the American Statistical Association is highly desired.” I’m not accredited by the ASA and don’t plan on it anytime soon, but if you want to be the Buck Rogers of Statistics, maybe you should. Continue reading