Author Archives: pmean

PMean: My research interests

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 →

Recommended: ENCODE: Encyclopedia of DNA Elements

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 →

Recommended: Lyndon B. Johnson Space Center job opening for a GS-14 Statistician

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 →

Recommended: A Grant Submission New Year’s Resolution

Michael Lauer, the Deputy Director for Extramural Research at the United States National Institutes for Health shows some interesting statistics on when people submit grants and shows that grants submitted earlier than the day of the deadline tend to fare slightly better in the review process. There’s one gross miscalculation on this page, but the message is still interesting. Continue reading →

Recommended: Points to consider on switching between superiority and non-inferiority

This page has moved to a new website.

One of the most confusing aspects of medical research is the difference between non-inferiority and superiority trials. This article explains in simple terms what the two type of trials are. Then it covers the desire of many researchers to switch from a non-inferiority trail to a superiority trial or vice versa. In general, if you would like to make the claim of superiority if the data justifies it, or to fall back on a claim of non-inferiority if you must, you are best off designing a high quality non-inferiority trial. The extra methodological rigor and the typically larger sample sizes that come with a non-inferiority trial make the transition from a non-inferiority hypothesis to a superiority hypothesis much smoother than the reverse. A high quality non-inferiority trial includes pre-specifying the margin of non-inferiority, demonstrating adequate power for the non-inferiority hypothesis, and justifying that the control group has demonstrated efficacy in previous trials. You need to show sufficient methodological rigor in your research design to establish that a non-inferiority finding is not just caused by an insensitive research design. Finally, you need to consider a “per protocol” analysis for the non-inferiority hypothesis, but switch to an “intention to treat” analysis for the superiority hypothesis. Continue reading →

Recommended: Differences between information in registries and articles did not influence publication acceptance

This page has moved to a new website.

Here’s a research article tackling the same problem of changing outcome measures after the data is collected. Apparently, this occurs in 66 of the 226 papers reviewed here or almost 30% of the time. The interesting thing is that whether this occurred or not was independent of whether paper was accepted. So journal editors are missing an opportunity here to improve the quality of the published literature by demanding that researchers abide by the choices that they made during trial registration. Continue reading →