Discount code

Written by a large bunch of great people, edited by Anna Heath, @NataliaKunst and me, and comes with an R package to use the methods at

Value of Information methods are used to determine the most important uncertainties in a complex model, and what we would gain from getting better information. They have been used a lot in health economics, but are more widely applicable. Based on Bayesian decision theory.

Some belated promotion for a new book "Value of Information for Healthcare Decision Making".

...Nearly forgot to add, I'm doing a talk about the survextrap package next week at RSS Northern Ireland https://t.co/AWBV21PDr1 . Teams link

The package should also be useful as an easy way to fit a flexible Bayesian survival model in general, even if you don't need extrapolation. It is inspired a lot by rstanarm , and tweaks the model in https://t.co/OyaF2Orlfi in various ways to facilitate extrapolation.

Hoping people will try it and use it. Please feed back, in particular if you would like to use it, but something about it looks too hard! The idea is to make principled methods easily accessible.

You can (a) build in multiple data sources (e.g. trial, registry, population, elicited...) in a general format (b) fit models with a single command, (c) output results with a single command, in a friendly tidy format.

So the model output is a posterior which says what your data says, and represents uncertainty properly if there is no long-term data.

The paper introduces an R package https://t.co/joidaieqL1 that does all this. It uses a flexible Bayesian model, fitted with https://t.co/Hi6venW62F. The "hazard" of death is allowed to vary over time using a spline, and can vary smoothly outside the data.

Ideal tool would be able to (1) combine several data sources (2) fit it all well (3) acknowledge uncertainty where data are weak. And not forgetting: (4) be easy to use!

Health policy decisions often involve "extrapolating" short term data from trials. To do this properly, we have to build in long term information. There's been lots of methods papers on the topic, but no ideal tool.

Announcing a new paper "survextrap: a package for flexible and transparent survival extrapolation" https://t.co/gpQCutCHJ2 . Proud of this, as I've been interested in the problem for many years, but only recently had time to get stuck into a solution. Tweet summary below.

Learn Bayesian statistics with us, wherever in the world you are!

3yr postdoc job opportunity @MRC_BSU @Cambridge_Uni in Bayesian methodology Potential topics/directions: computational methods for data integration/Markov melding; methodology for prior specification; methods for multi-stream EHR data Closes 6 Nov https://t.co/vBl96y0REP

(A department neighbouring the one that I work in, I meant to say)

If you like multistate / survival modelling you may be interested in this position https://t.co/QLZSd5xECX collaborating with some good people in a neighbouring department.

Not that I think this was a highlight of #RSS2023Conf, but slides from my talk about survival extrapolation are linked from

Coming to the end of an enjoyable three days of #RSS2023Conf . Too much good science and people to pick highlights, just generally feeling that stats and statisticians are alright!

"At first I was afraid, I was petrified"... An innovative method to the problem of extrapolating survival curves from heavily censored trial data: @ZhaojingC, @gianlubaio, @n8thangreen https://t.co/kARpkLDuJy