presize #rstats package up on CRAN!! 😃 Use for precision based sample size calculations. Intended for #clinicaltrials, but also useful for other disciplines #SCTOstatsmethodology @CTUBern
@R_Graph_Gallery@JohnHelveston if you need an example in favour of base, i had a case recently of using regex to extract text from complex strings. Using stringr was rediculously slow, but regmatches worked well. rewriting the pipeline in base improved performance a lot...
New paper just out: we tend to always use the Hazard Ratio as population-level summary measure in clinical trials with survival outcomes. Is that always justified? [1/N] @tmorris_mrc @IanWhit25399993@MaxParmarMRCUCL
https://t.co/X6LvLlNPY9
Over the course of 3 days scientists pumped 10 tons of cement into an abandoned ant hill. After weeks of digging, the colony’s intricate & impressive structure is revealed. This one
[full video, from "Ants! Natures Secret Power": https://t.co/ol5WJZU6A8]
https://t.co/G71KrCYafP
@Jake_Elder52@IsabellaGhement Mutate recently gained a by argument, so df %>% mutate(gcent.iv = scale(iv), .by = g) should also work. Don't need to worry about ungrouping then either...
@giulianonetto @JWoodelius you could use restricted mean survival time (how much time did they survive more within a specific timeframe on average than the other group)