Thank you for your excellent video. I have watched many videos on implementing propensity score models in R, but yours was by far the most clear and understandable. I truly appreciate it.
Wow. That's amazing. This is propaply the best tutorial in explaining propensity score analysis on RUclips. Thank you so much for your work. You literally saved my life. :D :D
after calculating IPW, do we need to add the confounding variables back to lm to control after the weight has been provided? Thank you for the explanation. the video is very helpful.
Thanks you. Around 9 minutes in, I would probably use mutate(ps= predict(model, type='reponse'). Much easier and less typing to just use the model and run predict()
If there are various estimates from adjusted regression, PS, and IPW all of them are significant and in the same direction. Is there any statistical tests to decide which one is correct or closer to the truth?
Yes, the default link function for family = binomial is the logit function. Only if we intend to use another link function do we need to specify otherwise.
extremely useful video for me. Thank you. However, I would like to ask how did you convert net into a new numeric column called net_num? I have a variable with "yes" and "no" which i had converted into factors with "as.factor" function. But that is not working for generating IPWs. please help
Thank you for your excellent video. I have watched many videos on implementing propensity score models in R, but yours was by far the most clear and understandable. I truly appreciate it.
Your videos are extremely helpful, thank you so much for making these!
Wow. That's amazing. This is propaply the best tutorial in explaining propensity score analysis on RUclips.
Thank you so much for your work. You literally saved my life.
:D :D
Kudos+++ for this clear and well-demonstrated concept of matching and IPW ! Thanks
Fantastic procrastination before my Epidemiologi exam! Thank you :-)
I'm veryyyyyy happy with your explanation! Keep doing this videos!
This is super helpful! Thanks for the video!
Really helpful. Greetings from Spain, Viva España.
Thank you SO much sincerely ! 감사합니다 !
after calculating IPW, do we need to add the confounding variables back to lm to control after the weight has been provided? Thank you for the explanation. the video is very helpful.
Thanks you. Around 9 minutes in, I would probably use mutate(ps= predict(model, type='reponse'). Much easier and less typing to just use the model and run predict()
Thanks for this video very helpful
what is the package used for ipw
Very good video
Is there the possibility of combining differences in differences with matching propensity score?
If there are various estimates from adjusted regression, PS, and IPW all of them are significant and in the same direction. Is there any statistical tests to decide which one is correct or closer to the truth?
Thanks so much
Can we just add family=binomial without link="logit" at 3:27?
Thanks a lot for this great lecture
Yes, the default link function for family = binomial is the logit function. Only if we intend to use another link function do we need to specify otherwise.
extremely useful video for me. Thank you. However, I would like to ask how did you convert net into a new numeric column called net_num? I have a variable with "yes" and "no" which i had converted into factors with "as.factor" function. But that is not working for generating IPWs. please help
Do anyone know what to do if you have a multinomial distribution? I have 4 potential outcomes.
Gracias