ACRM 2022 IC17: Longitudinal Data Analysis Using R: Part I Introductory Topics

By:
Allan J. Kozlowski, PhD, B.Sc. (PT) - Consultant, Mary Free Bed Rehabilitation Hospital
Keith Lohse, PhD, PStat - Associate Professor, Washington University School of Medicine
Video walk through of the data and code from Part 1 of the course:
github.com/kei...
See specifically handout 1: github.com/kei...

[@bipinthapa3338]

Thank you for such a lucid explanation and visualization of longitudinal analysis

[@YEZHANG-s9w]

Thanks so much for such wonderful video! I always confused by the treatment coding of the interaction in R. Thanks for explaining this!

[@michelleleemans7015]

Thank you very much for these clear explanations.

[@artaaraz]

Thank you for these wonderful videos, they are very helpful. Just a small comment: it would be very helpful to discuss in which situation which model should be used. For example, when the researchers should use a nonlinear negative exponential mixed effect models and how they should choose random and fixed effects. Unfortunately, I did not have time to go through all the videos. Please skip my comment if you have already discussed these points.

[@richaka]

Great work Dr. Keith Lohse

[@lohsifer]

Thank you very much. Glad it was helpful!

[@craighansen2818]

Great video and course materials, thanks for sharing.

[@WinnifredIk]

Thank you so much. I have a question, when i was taught longitudinal analysis, my professor used the lme() function and utilized correlation structures (exponential, unstructured, ...e.t.c). What is the difference between the lme() and the lmer().

[@rejuanhaque7064]

Great video!! I love the explanations!

[@marcogelsomini7655]

15:45 nicee explanation, thanks!

[@deltax7159]

was trying to follow along but the data you are working with is not the same data in the session1 data file for both 2018 and 2021. where is the data you are working with in the video?

[@eyadha1]

Thank you very much for this very helpful video.

[@lohsifer]

Glad it was helpful!

[@espnunez]

Thank you so much!!

[@caviper1]

gracias

[@camilaramirezzapata994]

ccol. thanks

[@lohsifer]

Thank you. Glad it was helpful!