Last month, I was invited to Melbourne for a pharmacometrics conference: Population Approach Group of Australia & New Zealand (PAGANZ) meeting. It was great getting to know some more pharmacometricians and really digging into the problems they face, specifically in statistical inference as applied to PK/PD models.

It was a busy trip with talks / workshops lined up on all three days:

I wanted to fill in some of the motivation, especially for the pace of the short course.

Stan course

Slides (pdf)

We were invited to give a one day Stan course as part of the intermediate workshop for pharmacologists. I’ve given this type of course about a dozen times now and it’s evolved each time I’ve taught it.

First, it’s really ambitious to try to learn a programming language in one day. It’s even harder when the output isn’t deterministic and there are statistical concepts needed that may not be used day-to-day. My goal for the course was to provide enough information where the students could continue to learn after the course.

At a high level, the course covered:

While that describes the content of the course, I used the time as an opportunity for the students to learn things that aren’t in the Stan Manual or are easier to learn in person. Here are some of the things I focused on:

After these courses, I’m usually asked why I don’t provide working examples to all the students. I’ve done that once and I found that to be one of the biggest impediments to gaining competency in Stan. There is definitely a different type of course that could be taught that is aimed entirely at interpreting Bayesian inference in Stan. This course was aimed at students that want to write statistical models.

Talk: “Stan Meets Pharamcology”

Slides (pdf)

This was a fun talk for me. I’ve been working on Stan for the last 7 years while keeping track of algorithmic and implementation developments in computational statistics and machine learning for the last 10. It was a chance for me to talk about how Stan has transformed statistical computing and some of the pharmacology applications Generable has been involved with recently.

I completely mistimed the talk and ended early. Fortunately, the talk sparked a lot of discussions about Bayesian inference and how it applies to PK/PD modeling and we were able to have a conversation about some of the finer points.

Talk: “Understanding lp__”

Slides (pdf)

This was for the Melbourne Stan meetup. I had wanted to discuss how lp__ is computed in Stan for a while. The exact computation is covered in the manual, but for many users, it’s still a mystery. We spent the hour understanding what lp__ is computing, writing the expression on paper for a simple model, writing an R program to compute it, and verifying the result through Stan. I’ll have to give this talk again soon.

The PAGANZ community was great and hopefully I’ll have a chance to collaborate with a lot of the members soon. There are a few people I want to thank for making this trip happen: