Bayesian statistics

BCEAweb is a front-end that can be used to access many of the functionalities of the R package BCEA. It can be accessed here. Files The landing page of the web-app has a very short tutorial proving a very brief guide and pointing to files that can be downloaded to test its functions. These are Vaccine_spreadsheet.csv: a spreadsheet with simulated values for all the model parameters for the “Vaccine” fictional example, discussed in the BCEA Book.

Introduction SWSamp is an R package designed to allow a wide range of simulation-based sample size calculations, specifically (but not exclusively!) for a Stepped Wedge Trial (SWT) and is based on the general framework described in Baio et al (2015). In its current version, SWSamp consists of 5 main functions: the first one (which is currently in fact specified by three different commands) performs the analytic sample size calculations using the method of Hussey and Hughes (2007).

Integrated Nested Laplace Approximation R-INLA is an R package that implements Integrated Nested Laplace Approximation (INLA), a method to perform approximate Bayesian analysis for a wide class of model specifications, including hierarchical regression models and spatial or spatio-temporal models. The idea underlying INLA is that, instead of performing computation for the posterior or predictive distributions using MCMC (which is generally very effective, but can be very computationally intensive, especially for complex models or very large datasets), in a specific class of models in which the prior distribution for the (vector of) parameter(s) is characterised by Gaussian Random Markov Fields, these tasks can be performed using approximations based on Laplace methods.

I think it’s now official: after a couple of years in which Covid has ruined everything, we’re now returning with our summer school on Bayesian methods in health economics! Of course, so many bad things have happened that this can hardly count as a major problem, but I have really missed the good fun we’ve always had in the week-long course, in Florence.

I’ve made a major refactoring of (the development version of) survHE. I guess one of the main issues with the package (both from the point of view of the user and the maintainer) was that survHE is a big package and installation is a very lengthy process. And this is no surprise: the trade-off here is between the massive savings in computational time that are obtained by pre-compiling the Bayesian models available (through rstan) and the time it takes to get everything installed on your machine… And, from the developer’s point of view, often submission to CRAN has been a pain, because some of the files that get installed are very large and, again because of the nature of the package, there’s quite an intricate structure of “dependencies”, which makes the package very heavy.

This week has been a fantastic one. I went to Cambridge to give the 18th Armitage Lecture; I have to say, when I was invited back in the summer, my first reaction was of course that I was very, very pleased and honoured. Looking at the list of previous Lecturers was pretty daunting, though — which in a way only increased the honour, while also increasing slightly the pressure… 😉

Final time — the model (and bookies) predictions have indeed materialised and Italy face England in the final. I’ve done the final update after the semi-finals and here’s the model prediction. Italy are favourite — I think this is probably right, though I also believe it’ll probably be a slightly closer game. The bookies go the other way and, I think a bit overly-enthusiastically, tip England to be just above Italy (the odds map to probabilities of about 31% for Italy to win, 37% for England to win and 32% for a draw).

I haven’t posted the results for the quarter-finals ahead of the games, but I did run the model and here were the predictions. Not too bad, in the end: the model did get that Spain-Switzerland would be a tough one and the resulting 1-1 was one of the most likely outcomes. Similarly, while giving Belgium a bit of an edge, the model had predicted that their clash with Italy would be very close and the eventual 2-1 win for the Azzurri (yay!

Rob (who, together with his team, has been brilliant in the process of publishing and managing BMHE and BMPR) has alerted me to the fact thatour edited book “Bayesian Methods in Pharmaceutical Research (Chapman & Hall/CRC Biostatistics Series)” (said “BMPR”), made it to BookAuthority’s Best New Bayesian Statistics Books. The official blurb says that BookAuthority collects and ranks the best books in the world, and it is a great honor to get this kind of recognition.

OK: I’ve now re-run the model using the updated data including all the games after the group stage. Firstly, a few comments/caveates: I think the model predictions for the round of 16 make kind of sense and I’m reasonably happy with them. BUT: there’s lots that the model doesn’t really know… For example, I’ve made no real adjustment for the fact that this batch of games is at the knockout stage, which means that, probably, we may see fewer goals as some teams may have a different attitude and would rather not lose the game at the 90th minute, in search of penalty glory.