It is known that there is regional variation in the rate of decreases in serotonin 1B receptor BP. So we checked the variations from the mean age effect, and even here, our outcomes were highly replicable.

Despite all these differences, our model yielded highly similar estimates of the association between model parameters and age across datasets. We also found that we could also successfully combine and harmonise datasets in the same model using covariates to improve performance further.

We also show large improvements to inferences made. Here, estimating simulated treatment effects relative to a placebo intervention. A: large increases in power, and B: our model's estimates of treatment effects are closer to the true value across simulated datasets

We show dramatic improvements in quantification using SRTM, reducing the RMSE by over 50% on average (and up to 90% for one region) for BPND, but also for the other estimated parameters too.

Day 5 of #AdventOfCode with #rstats : "Print Queue" (adventofcode.com/2024/day/5). Not pretty, not elegant and not particularly quick -- but it works! This one was quite fun: I can't remember the last time I used a while loop!

Day 4 of #AdventOfCode - "Ceres Search" with #rstats. I found this one pretty tricky! Had to dig into the old base R toolbox. Really interested to see how this could be done in a more #tidyverse way.

Day 3 of Advent of Code 2024: "Mull It Over" with #rstats . As bad of a reputation as it gets, I do love me some regex!!

#AdventOfCode

gist.github.com/mathesong/35...