Category Archives: statistics

Journal Club: Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.

Last week, we read Chien et al, Efficient mapping and geographic disparities in breast cancer mortality at the county-level by race and age in the U.S.  I’ve been very interested in these sort of “small-areas” spatial statistical methods recently, so it was good to see what is out there as the state of the art.  I think I’ve got something to contribute along these lines some day soon.

Maps in small multiples look just lovely, too:
usa

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MCMC in Python: random effects logistic regression in PyMC3

It has been a while since I visited my pymc-examples repository, but I got a request there a few weeks ago about the feasibility of upgrading the Seeds Example of a random effects logistic regression model for PyMC3. It turns out that this was not very time consuming, which must mean I’m starting to understand the changes between PyMC2 and PyMC3.

See them side-by-side here (PyMC2) and here (PyMC3).

pymc3

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IHME Seminar: Unifying the Counterfactual and Graphical Approaches to Causality via Single World Intervention Graphs (SWIGs)

Thomas Richardson gave a recent seminar at IHME about how the potential outcomes crowd can make sense of graphical models and vice versa. It also has a CSSS working paper to complement it, a trend in our recent seminars: http://www.csss.washington.edu/Papers/wp128.pdf

This talk had some great graphics, as I would hope for in a talk on graphical modeling:
graphical_model

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IHME Seminar: Bayesian reconstruction: estimating past populations and vital rates by age with uncertainty in a variety of data-quality contexts

A recent IHME seminar by Mark Wheldon described a Bayesian approach to estimating past populations and vital rates by age. I like this stuff. The talk is online, and there is a CSSS working paper on it, too: http://www.csss.washington.edu/Papers/wp117.pdf‎

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Filed under global health, statistics

Journal Club: Repeat Bone Mineral Density Screening and Prediction of Hip and Major Osteoporotic Fracture

Last week we read Repeat Bone Mineral Density Screening and Prediction of Hip and Major Osteoporotic Fracture by Berry et al. It argues that repeat screening does not improve predictions. But I think the world needs a better way to measure the quality of predictions like these. Area-under-the-curve doesn’t cut it when you are predicting the unpredictable.

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Filed under global health, statistics

Journal Club: Meat, fish, and esophageal cancer risk: a systematic review and dose-response meta-analysis

For our final journal club paper of last semester, we read Meat, fish, and esophageal cancer risk: a systematic review and dose-response meta-analysis. I am a vegetarian.

That brings me up to date for fall quarter, and the winter quarter is just finishing its first week. Good!

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What qualifies as probabilistic programming?

I just went through the classic paper on WinBUGS, which might or might not be called probabilistic programming. It is listed on the probabilistic programming resource page, and it is certainly interesting. The WinBUGS “hello, world” is a linear regression model:

regression_hello_world

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Filed under software engineering, statistics

Journal Club: Prognosis of patients with HIV-1 infection

Oh, how the quarter gets away. What happened in our journal club since I last recorded a paper? Well, I will start catching up now. The first thing that happened was just over a month ago we read a paper on prognosis for lovers of survival curves: Prognosis of patients with HIV-1 infection starting antiretroviral therapy in sub-Saharan Africa: a collaborative analysis of scale-up programmes. The author’s interpretation of their results:

INTERPRETATION:
Prognostic models should be used to counsel patients, plan health services, and predict outcomes for patients with HIV-1 infection in sub-Saharan Africa.

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MCMC Convergence Diagnostics

I have revisited my approach to deciding if MCMC has run for long enough recently, and I’m collecting some of the relevant material here:

Last time I thought about it: http://healthyalgorithms.com/2010/04/19/practical-mcmc-advice-when-to-stop/

Original paper for R_hat approach: http://www.stat.columbia.edu/~gelman/research/published/itsim.pdf

Presentation comparing several approaches: http://www.people.fas.harvard.edu/~plam/teaching/methods/convergence/convergence_print.pdf

Published comparison: http://www.jstor.org/stable/2291683

Blog about a cool visual approach: http://andrewgelman.com/2009/12/24/visualizations_1/

Discussion on cross-validated: http://stats.stackexchange.com/questions/507/what-is-the-best-method-for-checking-convergence-in-mcmc

Book with a chapter on this referenced there: http://www.amazon.com/dp/1441915753/?tag=stackoverfl08-20 (available as an eBook from UW Library, how convenient!)

Another related blog: http://xianblog.wordpress.com/2012/11/28/mcmc-convergence-assessment/

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IHME Seminar: Emily Fox on Bayesian Dynamic Modeling

This week we had a seminar on Bayesian Dynamic Modeling from UW Stats professor Emily Fox. The video is archived here! This talk had some of the most successful embedded video that I’ve seen in a talk. I don’t think it made it into the video perfectly, though, so imagine dancing honeybees while you watch.

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Filed under machine learning, statistics