All of the lecture content will be posted to Learn in this section. This page will remain updated as a way to help you access this content in an organized fashion.
If you have any questions about anything discussed in the lecture videos, please feel free to send a message on Teams or book an office hour slot to discuss. If you would rather ask your question anonymously you can access an anonymous survey on Learn to ask questions whenever. I will keep an updated Q&A on the course website.
This following schedule lists the recommended videos to watch, based on the progression throughout the term.
Note: do not feel obligated to follow the schedule as outlined; you are welcome to adjust to fit your own needs. Doing this will ensure that you stay on track to complete all of the course assessments.
Before beginning into the lecture material for the course, you may want to watch 001. Welcome to STAT 437 as an introduction to the course material, a discussion of the syllabus, course expectations and so forth. If you have any questions, please contact me!
| Week | Dates | Videos to Watch (Runtime) |
| 1A | January 5 - 7 |
002. What are Longitudinal Data? (28:56) 003. Exploring Longitudinal Data (Application) (39:48) Total Runtime: 1:08:44 |
| 1B | January 10 - 14 |
004. Notation for Longitudinal Data (Theory) (20:45) 005. What is Linear Regression (Review; Theory) (11:49) 006. Continuous Longitudinal Data: Why Can't we Just Use Regression? (Linear Marginal Models) (1:09:15) 007. Linear Marginal Models: Likelihood, Inference, and Asymptotics (Theory) (42:54) 008. Linear Marginal Models: Implementation in R (Application) (50:33) Total Runtime: 3:15:16 Note: This content can be pushed into Week 2 as well, which will have less new content. |
| 2 | January 17 - 21 |
009. What are generalized linear models? (Review; Theory) (21:14) 010. Marginal Models: Accommodating non-continuous outcomes (16:34) 011. M-Estimation: A Practicing Statistician's Best Friend (31:34) 012. Generalized Estimating Equations: Estimating parameters from Marginal Models (32:59) 013. Generalized Estimating Equations: Examples of GEEs (28:52) 014. Generalized Estimating Equations: Details of Asymptotic Inference (23:44) Total Runtime: 2:34:57 |
| 3 | January 24 - 28 |
015. GEE in Practice: Example on COVID-19 Test Positivity Data (40:18) 016. GEE in Practice: Example on Epilepsy Trial (32:33) 017. From the Population to the Individual: Mixed Effects Models (19:22) 018. Linear Mixed Effects Models (36:16) Total Runtime: 2:08:29 |
| 4 | January 31 - February 4 |
019. Linear Mixed Effects Models (Theory) (40:39) 020. Variance Testing Considerations: Constrained LRT (13:26) 021. Linear Mixed Effect Models (Application) (47:24) 022. Transition Models for Categorical Longitudinal Data (25:00) Total Runtime: 2:06:29 |
| 5 | February 7 - 11 |
023. Transition Models (Theory) (41:40) 024. Transition Models (Application) (50:42) 025. Handling Missing Data in Longitudinal Models (51:08) Total Runtime: 2:23:30 |
| 6 | February 14 - 18 |
026. Handling Missing Data in Longitudinal Models - MCAR, NMAR, and Likelihood Techniques (31:00) 027. Handling Missing Data in Longitudinal Models - Imputation and Weighting (35:37) 028. Recap of Longitudinal Methods (16:31) Total Runtime: 1:23:08 |
| Reading Break from February 21 - 25. Take the time for yourself, no need to work on STAT 437! | ||
| 7 | February 28 - March 4 |
029. Introduction to Time-to-Event Data (24:58) 030. Quantities of Interest for Survival Analysis (31:16) 031. Discrete Time to Event Data (26:54) 032. Discrete Time to Event Data (Theory) (41:05) 033. Discrete Time to Event Data: Exploration (Application) (28:34) 034. Logistic Regression and Proportional Odds Models (19:46) Total Runtime: 2:52:33 |
| 8 | March 7 - 11 |
035. Logistic Regression and Proportional Odds Models (in R) (26:15) 036. Introduction to Continuous Time Survival Analysis (25:32) 037. Continuous Time Survival Analysis: Likelihood Construction (Theory) (24:03) 038. Location Scale Family Distributions and Log-linear Regression (Theory) (16:23) 039. Continuous Time Regression Models in R using Survreg (Application) (28:16) 040. Accelerated Failure Time Models (11:41) 041. Accelerated Failure Time Models (Theory) (11:51) 042. Accelerated Failure Time Models (Application) (27:43) Total Runtime: 2:51:44 |
| 9 | March 14 - 18 |
043. Proportional Hazards Models (17:16) 044. Proportional Hazards Models (Theory) (15:32) 045. Proportional Hazards Models (Application) (37:53) Total Runtime: 1:10:41 |
| 10 | March 21 - 25 | No New Content. Focus on your Assignments. |
| 11 | March 28 - April 1 | No New Content. Focus on your Assignments. |
| 12 | April 4 - 5 | No New Content. Focus on your Assignments. |