Pausal Živference @PausalZ@qoto.org
- GitHub
- https://github.com/pzivich
- website
- https://pzivich.github.io/
Paul Zivich. Computational epidemiologist, causal inference researcher, and open-source enthusiast #epidemiology #statistics #python
I added badges that link to my articles in the repository that has publicly available code
Ahh yes, thank you ResearchGate. The paper I wrote is probably related to my interests, I'll be sure to read it
Here's a surreal (at least to me) story. So, I was messing around with the coding capabilities of GPT-3. I asked it to code TMLE in Python for me.
The weird part was that GPT started using the Python library I wrote to do TMLE. However, it got the syntax and how the functions wildly wrong. Like the import statements are not even correct
So if you're using GPT to code, you better be familiar with the libraries it calls (and that it can even call the correctly)
The more interesting contribution is the proposal of a way to combine statistical (e.g., g-methods) and simulation (e.g., mechanistic, math, microsim models)
Here, I will review the basic idea / motivation
In the paper, we have an illustrative example in STI testing. We want to generalize a trial to a clinic population. However, the trial was only conducted among men, but the clinic includes men and women
This violation of positivity prevents us from transporting
But let's consider the following structural model (where W=1 is women) provided in the image. Were this model known, then we could transport. However, we are only able to estimate the red part of the model using the data...
So we propose using a simulation model to fill-in the blue component. This simulation model is driven by external knowledge
In the paper, we show how the other two approaches to addressing positivity are special cases of the synthesis approach. G-computation and IPW estimators are proposed. Both are applied to an illustrative example and in simulations (code at link below)
https://github.com/pzivich/publications-code/tree/master/TransportNoPositivity
Lots of discussion of #ChatGPT but I already created a bullshit scientific abstract generator
I made this plot for my M-estimation library, but I think the image nicely showcases how the various loss functions for robust mean/regression penalize the errors (of the 1st deriv of log-L)
an easy way to spice up your statistical analysis #epidemiology #biostatistics
- GitHub
- https://github.com/pzivich
- website
- https://pzivich.github.io/
Paul Zivich. Computational epidemiologist, causal inference researcher, and open-source enthusiast #epidemiology #statistics #python
