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Really nice presentation. Straightforward and thoughtful.

nicely done! thanks for depicting this difference. The only thing - which I guess can be resolved by discussion within panels?! - is what to do when the numbers are not even (like 9 participants in your toy example).

Good afternoon. Suppose we have more than one predictor. How do we approach it?

Great

Great video, thanks!

This was really helpful, thanks! I have been wondering how we got exp(B1)! You did a great job explaining.

VERY GOOD

Underrrated channel.NICELY PRESENTED .THANKS FOR THIS VIDEO AND THE OTHER

If you don't have treatment switching, as in the above example where patients shift from the antibiotic treatment to surgery after some time, the easiest way to eliminate immortal time bias would be to considers the time point of treatment decision as baseline for all comparisons.

Great explanation. Thanks

hi, what dataset did you used?

Thank you for this nice talking in epidemiology, is it possible to illustrate the three methods with SAS / R code？

Hi, really interesting video. I am an MSc Epidemiology student. Can you please make an elaborate video on types of regression modelling and their appropriate use? Like which model to use where (depending on study type or the outcome and exposure)? Thanks and keep up the wonderful work!

Watching this for my biostats internship, great info !

good stuff

What is the SAS syntax to run modified Poisson regression? thanks

The video that helped me the most to understand this topic, Thank you!

I'm sorry, how are the individual B estimates calculated (treatment, sex, age)? I am confused... What does it mean controlling for the other two factors?

This is such a great video, you explained everything so clearly. Thank you!!🙌

Better than my professor

Thank you Sir

You’ve done a great job presenting this topic in an easily understood manner. Kudos!

Thanks

The DATEDIFF function you were trying to kick off is in SQL Server, not SAS. In SAS it's INTNX. Awesome video, learned alot from it!!!

Awesome, thanks mahn

This video is amazing! I’m so glad you didn’t edit it!!! You have no idea how much it helped me the fact you didn’t edit. I was getting errors and didn’t know my mistake until I watched your video. How do I graph those variables with price logistics?

You are my life-saver, thank you so much!

This is the best video on DAGs. Thanks a bunch!

Thank you very much

Hi, I'm the author of dagitty. Thanks for this tutorial! A few tips: - You can also clear the canvas by clicking "Model -> New Model" - After clicking on a variable, you can also type "r" to bring up the "rename" screen

Words can't express my sincere gratitude 🎉

explained very well. Helps to understand easily.

I could cry! Thanks so much for this! Its so simple!

If for lung cancer all three of smoking occupation and anxiety is an ancestor why not for smoking occupation and anxiety are ancestor instead of only anxiety that you mentioned? (6.35)

thank you, my friend, you really saved a lot of time

Please what is the name of this formula ?

Very helpful, thanks!

Love these videos! They're definitely helping me during my MPH.

Why is occupation not an ancestor/parent of smoking? At 6:45

👍

Great video

bullshitt teaching

Thank you for the slides, explained better than ChatGPT did. How do you deal w immortal time bias if the treatments are exclusive, though (i.e. for whatever reason, the surgery candidates cannot take antibiotics)?

Thank you, this is very helpful!

thanks it really helped me

Thank you!

Thank you so much, very informative. I want to ask how would I know the sensitivity and specificity in order to correct for any misclassification ?

You are the best

Thank you so much 🙏🏾