Firstly, Merry Christmas and a Happy New Year from Sydney!

Okay, now that’s out of the way I can admit that I’ve been extremely remiss in keeping this blog up to date. I’d say that I’d make a New Year’s resolution to keep it more up to date, but the cultural consensus seems to be that that might even be counterproductive.

Anyway, since I last made a personal update, a fair amount of stuff has happened:

So I’ve been using Vim recently for text editing and coding. I’ll write a post someday about why, but what really sucked me over from using Emacs was the awesome way that you can naturally build up commands with a kind of “verb-object” form.

Anyway, part of using an editor like Vim is building up a bunch of settings for your editor: things like remapping Caps Lock (AKA: the least useful key ever) to Esc (which you use all the time in Vim). These live in a little file in your home directory called .vimrc.¹

So naturally, when I fired up Vim at work and started writing, the first thing I get is

Look at me writing some text!
:WQ
::asd;lfsgihaspoihtasdophif

Suddenly, muscle memory is no longer your friend.

[This is the second of two posts I wrote on estimating the cost-effectiveness of the DCP organization.. It was originally published on the Giving What We Can blog.]

Warning: I’m now going to start assuming some serious statistics knowledge! Most readers should not expect to be able to follow the guts of what is being done, but the bottom line should still make sense. As before, we’re using R to do most of the work, and the script is attached.

In the previous post, I explained a simple statistical model for assessing the effectiveness of donating to DCP. In this post I would like to describe some improvements to the model. The most important of these is allowing for the possibility of error in DCP’s assessment process. Some of the variation in the DCP’s results are due not to real differences in the cost effectiveness of treatments, but rather simply measurement error on their part. We will run simulations where we vary how large this error is, and see how much it reduces the value of DCP’s work. The bottom line is that given guesses about the size of the errors and the funding their research moves, DCP still appears much better to fund than the best treatment they identify.

[This is the first of two posts I wrote on estimating the cost-effectiveness of the DCP organization, who themselves publish excellent reports on the cost-effectiveness of health interventions. It was originally published on the Giving What We Can blog.]

When searching for high-effectiveness charities, there are a few resources that incredibly useful. One of those is the reports of the Disease Control Priorities Project, which provide an analysis of the cost-effectiveness of a great number of health interventions.

Given the value of this data, we might wonder just how valuable this information is, and whether it might be worth donating to DCP to produce more of it.

In this post I will present a statistical model (designed by myself and Nick Dunkley) for estimating the effectiveness of donating to DCP. Although this is going to over-simplify the situation, it should be able to give us a ballpark estimate.

Around this time of year in Sydney they run a huge sculpture exhibition from Tamarara bay around the headland to Bondi beach. We had a work barbeque and expedition to see the sculptures, which were pretty awesome!