Verbal Autopsy methods earlier

A cool addition to the big verbal autopsy study I worked on a few years ago is out now: “symptomatic diagnosis” takes the verbal autopsy approach and applies it to find out what ails people non-fatally.


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Filed under global health

Kish Stuff

A student came by interested in survey statistics and we go to talking about what an amazing person Leslie Kish must have been. We did some googling on it. Here are a few items we found:

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Some material on making a world map in Python

I just want the outlines of selected countries… that should be easy, right?


Filed under software engineering

A new report from National Academies Press

I am flipping through yet another National Academy report this week. They know what hooks me. This time: What Research Says About Effective Instruction in Undergraduate Science and Engineering (2015).

Lots of ideas for little changes to my class in here…


I mean, not exactly what I will do, but lots of inspiration.

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Filed under science policy

Open and Reproducible Research: Goals, Obstacles, and Solutions

A set of slides from a talk by Matthew Salgnik crossed my inbox recently, titled “Open and Reproducible Research: Goals, Obstacles, and Solutions”. Good stuff! I liked the *bonus points* in the Data-is-available section:

bonus points for releasing extra variables that are not need to reproduce specific analysis.

This gets at what I think is really the point of reproducible research. To make it faster and easier to make new knowledge.

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To read: Modeling Good Research Practices

I wonder if this will be useful: Modeling Good Research Practices—Overview: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-1

It has quite a lot of best practices!

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Filed under disease modeling

Non-parametric regression in Python: Gaussian Processes in sklearn (with a little PyMC)

I’ve got a fun class going this quarter, on “artificial intelligence for health metricians”, and the course content mixed with some of the student interest has got me looking at the options for doing Gaussian process regression in Python. `PyMC2` has some nice stuff, but the `sklearn` version fits with the rest of my course examples more naturally, so I’m using that instead.

But `sklearn` doesn’t have the fanciest of fancy covariance functions implemented, and at IHME we have been down the road of the Matern covariance function for over five years now. It’s in `PyMC`, so I took a crack at mash-up. (Took a mash at a mash-up?) There is some room for improvement, but it is a start. If you need to do non-parametric regression for something that is differentiable more than once, but less than infinity times, you could try starting here:

p.s. Chris Fonnesbeck has some great notes on doing stuff like this and much more here:

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