Category Archives: Statistics

PMean: What are we doing to justify all that time we’re budgeting?

An email discussion about the appropriate percentage effort on research grants has produced a lot of interesting discussions. One person raised an interesting question. The typical data analysis, he claimed, might involve a few hours reviewing the input data set, a few hours conducting the analysis and a few hours preparing a statistical summary, but even after a generous estimate of the work at each of the time points, he could only come up with 22 hours of effort, which corresponds roughly with a 1% FTE. I wrote back describing some of the things that might occur before the data analysis that might add time to this effort. Continue reading

PMean: Draft policy on statistical support for research

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?

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Recommended: Definitions of Criteria and Considerations for Research Project Grant Critiques

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

Recommended: Guidelines for estimating biostatistician effort and resources on grants

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

Recommended: Statistical and Machine Learning forecasting methods: Concerns and ways forward

At first glance, you might think that this article looks like a vindication of traditional statistics. Classical time series models (methods that were available in the 1960′s) outperform newer machine language forecasting models. Then, you might worry that the comparisons were unfair. But neither viewpoint is accurate. The classical time series models have certain structural advantages for certain types of problems, but you might be better off with machine learning if you use classical time series as a preprocessing step, such as de-seasonalizing your data. If nothing else, this article provides a nice overview of some of the major machine learning methods. Continue reading

PMean: My teaching interests, one page limit

I have been applying to a variety of jobs, and some of them, mostly universities, want a statement of teaching philosophy, research interests, or some combination. I enjoy writing these, except for the ones that have page limits. In this and the next few blog posts, I will share what I wrote. If you read these, it might give you a better idea of what I do at my current and previous jobs and what I would like to do in a future job. Here’s a one page limit statement on my teaching interests and experience. It won’t be one page on my blog because of formatting differences, of course, but it will be brief than I like. Continue reading