We live in a golden age of learning, where you find find just about anything you’d ever need to learn from on the Internet. One example of this is a series of webinars about who to get research funding through the Congressionally Directed Medical Research Programs (CDRMP). I have not listened yet to any of these webinars, but they look like they would be very helpful for anyone seeking funding through this program. Continue reading
I dated a piano major in college and I tried, with very limited success, to learn how to play the piano myself. She told me, “If you’re going to make a mistake, make a loud mistake.” You don’t want to play the piano nervously and hesitantly. The same is true in research. Continue reading
It’s hard to find good examples of well-written research grants, so this website is wonderful. It shows examples of all sorts of NIH grants (R01, R03, R15, R21, R33, R41, R42, R43, R44, K01, K08, F31) as well as sample data sharing plans, biosketches, and reference letters. Continue reading
I am drafting up a policy on statistical support for research at my part-time job at UMKC. It is loosely based on standards at the University of California, Davis and Kansas University Medical Center. An early draft appears below. I’ve gotten some suggestions that setting a minimum percentage effort is a bad idea. What do you think?
I have to help write NIH grants from time to time, and I need to always keep front and center the criteria that NIH peer reviewers use when they evaluate grants. They look at five broad areas: significance, investigators, innovation, approach, and environment. This document explains what each of these five broad areas means. Continue reading
What percentage effort is reasonable for Biostatistics support on a research grant? The UC Davis Biostatistics Group says 10% as a bare minimum, 35-60% for straightforward projects with uncomplicated analyses, and 50-100%+ for large or complex projects. They give examples of large and complex projects: interim analysis, multi-site projects, development of novel statistical methods, and assembly of data from large, complex, or poorly documented administrative or survey data sets.
They also describe how to split the effort between a PhD Biostatistician, who supervises the overall effort, and a MS Biostatistician, who does most of the data management and statistical analysis.
Another point worth noting is that any grant listing less than 10% effort for a Biostatistician requires a special sign off. Continue reading
If you are writing a research grant, there are a lot of statistical issues that you need to consider. This guide, prepared by the American Statistical Association, highlights three areas: framing the problem, designing the study, and specifying the data analysis plan. It doesn’t talk enough about data management, but otherwise it is an excellent resource. Continue reading
I came across a question, “How does your institution incentivize researchers to write more grants?” that was posted a while ago. I felt it was too late to respond directly, but I did want to mention something in my blog about this. “Incentivize” is one of those awful words that used to be a noun (incentive) but has been changed to a verb to make it sound more trendy. That’s something to dislike from the very start, but I have an even greater gripe about incentivizing. Continue reading
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
This article provides guidance for developing the “statistical considerations” section of a research grant. I normally do not use that term, and suggest separate sections on statistical methods, sample size justification, data management plan, etc. But that’s a quibble. This is very good practical advice, such as reminding you that you need to write both for the statistical reviewer and the non-statistician who is also reviewing the proposal. Continue reading