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38:59 Shouldn’t it be the sum of the **absolute values** of the coefficients?

1:03:36 I think the difference is that the models being fit during hyperparameter tuning are fitted using (k-1)/k part of the training data. That’s because the last part is used for evaluation during the k-fold tuning. So when we know what the best hyperparameters are we want to fit the model using the whole training set.

Great information! Thanks for sharing this video.

What indentation style is used at 29:00? How would this look linted?

Thank you for your presentation. I wish I would have had opportunity to work on this transition. Really seems like it is so helpful to the company also helpful to SAS programmers to transition in their career as this is the way things are going. Look at the job requirements, all of these other languages are now, wanted.

Quarto is great! And even better than Latex can use Quarto with Typst!

Its great for learning. I am getting errors such as Deprecated etc. Have any updated one?

this is so cool!

Someone's an AEW Fan

Really great. Just wish you’d do things in base R. I’m an advanced user and avoid ggplot and tidyverse so wish you’d stop pushing those tools

What if you need to optimize two parameters from two different studies? For example, ka from oral bolus in vivo data and clearance from iv study? How do you optimize ka and clearance to fit both studies with one pbpk model?

Thanks for a great presentation!

O

That's fascinating! 🤩 Your team makes some of my dreams come true. I'll try my best to make the best use of it

cool

This is a very well constructed lecture and reproducible (all the data and source code is given on github). A nice scaffold to build your own lectures on and expand. My only complaint is that the transitions are often not clear and bit rugged (e.g. spend a lot time on LASSO but then use Logistic Regression).

If you are worried about which columns are picked in step_normalize() and you all want columns with values greater than 1 then I believe this code works: step_normalize(where(~is.numeric(.x) && any(.x > 1))). Now what the author uses in the video is more straight forward and thus simpler but if you have a lot of columns the first approach might be safer.

It is hard to sit there and look at a relatively small font (I know you can expand the view) on a light background and not get eye strain. I recommend using a dark background in RStudio.

If you are starting out with tidymodels then yo might be confused since a little of details are left out. Naturally you cannot cover such a big subject in a short lecture. Those needing more information might want to look at "Tidy Modeling with r" by Kuhn and Silge(2022). A free ebook version is available online.

Really nice talk. Thank you for pioneering this work!

Great video tutorial, looking forward for more {Teal} Tutorial

cool!

On running the following code snippet # Baseline Characteristics ---- adsl_bl <- pre_adsl %>% derive_vars_transposed( select(vs, USUBJID, VSTESTCD, VSSTRESN, VSBLFL), # Dataset to transpose and merge onto by_vars = vars(USUBJID), # Merge keys key = VSTESTCD, # Names of transposed variables value = VSSTRESN, # Values of transposed variables filter = VSTESTCD %in% c("HEIGHT", "WEIGHT") & VSBLFL == "Y" # Restrict records to just height and weight ) %>% # Do some cleanup rename(HEIGHTBL = HEIGHT, WEIGHTBL = WEIGHT) %>% select(-VSBLFL) %>% mutate(BMIBL = compute_bmi(HEIGHTBL, WEIGHTBL)) I am getting the following error. Error in `assert_list_of()`: ! Each element of `arg` must be an object of class/type 'symbol' but the following are not: ✖ Element 1 is an object of class 'quosure' --- Backtrace: ▆ 1. ├─pre_adsl %>% ... 2. └─admiral::derive_vars_transposed(...) 3. └─admiraldev::assert_vars(by_vars) 4. └─admiraldev::assert_list_of(arg, "symbol", named = expect_names, optional = optional) Run rlang::last_trace(drop = FALSE) to see 1 hidden frame. Can you please guide?

Great presentation. Thanks for sharing this.

good refresher, 😇

Can you add timestamps/sections to your video please?

This is excellent! I like the level of depth.

Thank you

Great demo, so informative! You explained Observable Plots really well.

This is amazing work!!

The reports are just amazing. Thank you for sharing!

I'm able to install ggsurvfit package, but cannot library it. any suggestions for the issue?

This is outstanding presentation, I find it very useful. Thank you Daniel! Wojtek. W.

Nice one!

💡

🎉 wat een goede presentatie

In 1997 CDISC was formed to harmonise data standards across the industry and there were just as many nay-sayers for that initiative. Their work continues and admiral's is just beginning by comparison. admiral has the potential to make a similar impact on the industry.

Thank you! I signed up for this course when it first came out and Ilove it. I look forward to taking your new course. As a biostatistician in public health I was desperate to learn pharmaceuticals workflow and their reporting system, there isn't enough resources unfortunately especially for R.

Connect with Michael on LinkedIn, if you’d like to continue the discussion after r/pharma: www.linkedin.com/in/michaelrimler/

Project GitHub: github.com/phuse-org/OSTCDA Join the Discussions and leave your perspectives, opinions, links, references, presentations, to help us cultivate a comprehensive digest of the current state of the industry using OS tech for clinical data analytics and reporting

Great talk!

Wonderful presentation

Amazing

github.com/agstn/RPharma23

Lovely explanation John!

3rd Module starts at 1:14:20 4th Module starts at 1:31:05 5th 1:42:12 6th 2:11:54

love it!

Thank you Micheal, Atorus Research and R in Pharma for conducting this workshop and posting it online!! This is super helpful!!

Thanks for this instructive and worable presentation.

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