Category Archives: advanced

Slides for a talk on software for Bayesian analysis

Here are some slides for an overview talk I gave earlier today on software for Bayesian analysis. What I should really do is embed some videos of screen capture demonstrations… one day.

Result: happiness

Featured: BUGS, JAGS, Stan, calling each of these from R, and some musings on C++, Julia and GPGPU.

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Training for analysing routine healthcare databases

This is a huge growth area: computerised health records bring “Big Data” to the world of medical research (even if you don’t believe Big Data is all that big or new). But opportunities to learn the tricks from the experts are rare, and believe me, there are a lot of tricks needed to get reliable findings out. So these three short courses at UCL (Royal Free Medical School) are selling out fast.

  • Course 1: An introduction to Primary care databases – Monday 5 November
  • Course 2: Missing data and new methods for multiple imputation of longitudinal electronic health records – Tuesday and Wednesday 6 – 7 November
  • Course 3: An introduction to Hospital Episode Statistics – Thursday and Friday 8 – 9 November

Trainers include members of the prolific THIN research team and experts in epidemiology, statistics and missing data. In particular, Course 2 is the only chance you’re going to get this year to learn two-fold FCS imputation, a new method for imputing longitudinal data without getting tangled up in horrendous multicolinear imputation models, so new the methods paper is not even out yet, and you can learn from the inventors! They also have a new Stata command to carry out the imputation called -twofold-

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Stan: new software for Hamiltonian Monte Carlo

As much as we love Markov Chain Monte Carlo as a flexible method for estimating all sorts of statistical models even when old-fashioned likelihood-based estimators aren’t available, nobody likes waiting till next Christmas for it to converge, having to throw away most of their massively auto-correlated steps on Boxing Day, or scratching his or her head at 2 a.m. when yet another set of initial values fails.

In recent years there has been a flurry of activity devising better algorithms that explore the parameter space efficiently and give you posterior distributions. One such is Hamiltonian Monte Carlo, and now Andrew Gelman and colleagues have released version 1 of new software that provides us with the first off-the-peg tool to try this technique out for ourselves! It is called Stan and its homepage is here. I wonder if that logo is inspired by Professor Gelman’s journey to work every morning… There is also an R interface called RStan with a useful quick start guide here.

Excuse me, does this go to 116th Street?
Yes, with probability 1 as time tends to infinity.

Now, I haven’t tried this out yet but initial reports say “reliable” and “very fast”. These are words I like to hear!

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Meeting: causal inference in observational data

This promises to be a good gathering in Manchester on 17  October 2012:

14.00 – 14.50: Professor Jonathan Sterne, Head of School of Social and Community Based Medicine, The University of Bristol

Causal inference for dynamic treatment regimens: how analyses of observational data changed international guidelines on when to start antiretroviral therapy

14.50 – 15.40: Dr Rhian Daniel, London School of Hygiene and Tropical Medicine

Causal mediation analysis with multiple causally-ordered mediators

16.00 – 16.50: Professor Kate Tilling, The University of Bristol

Examining associations between gestational weight gain, birthweight and gestational age using multivariate multilevel models

Register by email to  or 0161 275 5764.

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Stats in Medicine

The new issue of Stats In Medicine has a number of interesting-looking papers if you are into joint models, frailty models, or multistate models…

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