Biostatistics 236 Modeling Longitudinal Data

Spring 2024 - Robert Weiss

News & Announcements:

• Quick summary of useful graphics
• Suggestion: Wear a mask. Then we don't share infectious diseases (flu, cold, RSV, Coronavirus, and all the other named and unnamed infectious diseases that we share by breathing). 
• We have been moving to R for computer labs. Updates may be in progress 
• Data Analysis Project Comments and General Advice.
• Variance Covariance Choices. 
• Let me know if any errors.
• (Tentative) SAS Studio Introduction (word) Please follow instructions on first page and register for SAS Studio. Ideally immediately, but definitely before lab 8 Wednesday May 24th.
• 2024 Syllabus has been posted.

Table of Contents

• Class Schedule
• Syllabus and Textbook
• Lecture Notes
• Homework 
• Computer Labs in R
• Computer Labs in SAS
• Data Sets
• Discussion Questions
• Topics from lectures and labs 
• Zoom recordings of lectures/labs are available in the Media Gallery on Bruin Learn. 

2024 Class Schedule, Email Addresses, and Office Hours:

For Zoom links see Syllabus

Syllabus and Textbook

2024 Syllabus has been posted. Includes 2024 HW due dates and 2024 grading system. Includes Zoom Links. 

The 2024 textbook is Modeling Longitudinal Data. The book web page has data and links to example code.

The textbook is available for download through the UCLA library Springer bookshelf. If you are off campus, you will need to VPN to campus to be able to download the book. When I type "Modeling Longitudinal Data" (without the quotes) into the search box at UCLA Library webpage, the book is the first item on the list.

Lecture Notes

• Zipped folder of all 9 course note packets but don't have the minor edits. Password Protected.
• Lecture notes 1 intro.
• Lecture notes 2 graphics. Quick summary of useful graphics
• Lecture notes 3 simple critique MVN.
• Lecture notes 4 predictors.
• Lecture notes 5 covariance.
• Lecture notes 6 random effects.
• Lecture notes 7 examples missing computing.
• Lecture notes 8 computation examples discrete.
• Lecture notes 9 bivariate.

Variance Covariance Choices.

Homework Assignments

Homeworks (PDFs)

Please turn in PDFs of your solutions on Bruin Learn. Due dates are on the syllabus.

All data Sets in both Text and SAS Formats

• Homework Number 1 
• Papers for HW #1 problem 1
  • Counsell et al (2007)
  • HLP (2007)
  • Ponsford et al (2014)
  • Rabiolo et al (2020)
  • Li et al (2021)
• Homework Number 2
• Homework Number 3
• Homework Number 4
• Homework Number 5
• DAP1 Macular Data Analysis
  • Macular Data SAS file
  • Macular Data Excel file
  • Macular Data .csv file
  • Macular Data .txt file
• Final Data Analysis Project (FDAP).

Homeworks (Original LaTeX)

• Homework Number 1
• Homework Number 2
• Homework Number 3
• Homework Number 4
• Homework Number 5
• DAP1 Macular Data Analysis
• Final Data Analysis Project (FDAP)

Computer Labs in R for Learning Longitudinal Data Analysis

• Lab 1 Data Management and Introduction
  • RLab 1 Intro.docx
  • Lab1.R
  • Pediatric Pain data (text file)
  • Cognitive Data Set for TODO portion of lab.
• Lab 2 Graphics
  • RLab 2 graphics.docx
  • Lab2_graphics.R
  • bigmice data
  • smallmice data
• Lab 3 Simple Analyses + 1 Longitudinal Random Effect Model
  • Lab3_simple_analyses.docx
  • Rlab3 _simple_analyses.R
  • Pediatric Pain data (text file)
• Lab 4 Longitudinal Models for the Pediatric Pain Analysis. 
  • R Output
  • R Commands
  • Pediatric Pain data (text file)
• Lab 5 Covariance Models for the Small Mice Data 
  • Handout
  • R Commands
  • Selected R Output
• Lab 6 Hierarchical Models BSI data and Weight Loss Data
  • Handout
  • R Commands
  • Selected R Output
• Lab 7 Longitudinal Models for Discrete Data
  • Spring 2024 (only) Meets on Monday instead of Wednesday
  • Handout
  • Selected Output
  • R Commands
• Lab 8 Using SAS to fit Covariance Models
  • SAS Lab 5 (word)
  • SAS Lab 5 selected output (word)
  • SAS Lab 5 Questions and Answers (word)
• Lab 9 Residual Analysis for Random Effects Models
  • Lab 9 (word)
  • Pediatric Pain SAS File
  • Weight Loss SAS File
  • SAS output, word document (warning: 56 pages long!) (word)

Computer Labs for Learning Longitudinal Data Analysis Using SAS

• Lab 1 Initial Exploratory Data Analysis
  • SAS Studio Introduction (word)
  • Lab 1 (word)
  • Pediatric Pain data (text file)
  • Cognitive Data Set for TODO portion of lab.

• Lab 2 Graphics
  • Lab 2 (word)
  • Pediatric Pain SAS data set for use in Lab 2. (word)
  • Mice Big (Balanced with missing format)
  • Mice Small
• Four needed Macros (written by Michael Friendly)
  • boxanno.sas
  • gdispla.sas
  • gensym.sas
  • scatmat.sas
• Lab 3 Simple Analyses
  • Lab 3
  • Lab 3 selected output
  • Pediatric Pain Sas data set.
  • Example code for Dental Data
• Lab 4 Fixed Effects: Class variables and estimate, contrast and lsmeans statements
  • Lab 4(word)
  • Lab 4 comments (word)
  • Pediatric Pain Text File
• Lab 5 Covariance Models
  • Lab 5 (word)
  • Lab 5 selected output (word)
  • Lab 5 Questions and Answers (word)
• Lab 6 Hierarchical/Nested Data
  • Lab 6 (word)
  • Selected output and comments (word)
  • BSI Total
  • Weight Loss 2 (day of observations, visit type)
• Lab 7 Discrete Data
  • Lab 7 (word)
  • NLMixed and Glimmix Malaria output (word)
  • SAS Glimmix Procedure (links at bottom for the procedure and for the documentation).
  • Morbidity (Kenya)
• Lab 8 Bivariate Longitudinal Data
  • Lab 8 (word)
  • Pediatric Pain Text File
  • Selected output and comments 1 of 2 (word)
  • Selected output and comments 2 of 2 (word)
  • Lecture notes on bivariate longitudinal data
• Lab 9 Residual Analysis for Random Effects Models
  • Lab 9 (word)
  • Pediatric Pain SAS File
  • Weight Loss SAS File
  • SAS output, word document (warning: 56 pages long!) (word)

Data Sets

• Data Sets in both SAS and Text Format

Discussion Questions

• Discussion question 2.
• Discussion question 1.

Lecture Contents

Lecture recordings posted to our BruinLearn Media Gallery (near bottom of BruinLearn menu bar).

Lecture topics from 2024 lectures to-date. For future dates, lecture topics are from previous year. 

• Syllabus (non-lecture, pdf).
• Lecture 01 Monday 2024_04_01. Syllabus. Lecture notes 1 intro pages 1-11. Lab 1 and lab 1 data sets. 
• Lecture 02 Wednesday 2024_04_03. Lecture notes 1 pages 12-21 (Small Mice Data). 
• Lecture 03 Monday 2024_04_08. Lecture notes 1 pages 21- 28. Lecture notes 2 Graphics pages 1-33. 
• Lecture 04 Wednesday 2024_04_10. Lecture notes 2 Graphics pages 33-46. 
• Lecture 05 Monday 2024_04_15. Lecture notes 2 pages 47-49. Lecture notes 3 simple methods pages 1-17. 
• Lecture 06 Wednesday 2024_04_17. Lecture notes 3, multivariate normal model pages 18-30. 
• Lecture 07 Monday 2024_04_22. Lecture notes 3 pages 31-48. Lecture notes 4 specifying predictors pages 1-10. 
• Lecture 08 Wednesday 2024_04_24. Lecture notes 4 pages 8-10. Handout of annotated output for lab 4, pages 1-7. 
• Lecture 09 Monday 2024_04_29. Lecture notes 4 pages 11-26 (top of page 26). RLab 5 selected output. 
• Lecture 10 Wednesday 2024_05_01. Lab 5 covariance modeling.
• Lecture 11 Monday 2024_05_06. FDAP mention. Lecture notes 4 pages 26-40. Lab 6. 
• Lecture 12 Wednesday 2024_05_08. Plots from lab 6. Lecture notes 4 pages 41-53. 
• Lecture 13 Monday 2024_05_13. Output from lab 7. Lecture notes 8 pages 14-35 on discrete outcomes. Lecture notes 4 pages 53-61.  
• Lecture 14 Wednesday 2024_05_15. Finished lecture notes 4 covariates. Started lecture notes 5 covariance modeling pages 1 to 10. Sound was off for a likely significant portion of the lecture, very sorry. 
• Lecture 15 Monday 2024_05_20. Lecture notes 5 covariance modeling, pages 11 to 30. Lab 8, which is the covariance model fitting lab 5 from the SAS modelings. 
• Lecture 16 Wednesday 2024_05_22. Extended lab introducing SAS proc mixed. 
• Lecture 17 Monday 2024_05_27. Memorial Day Holiday
• Lecture 18 Wednesday 2024_05_29.Lecture notes 5 covariance modeling, pages 35-42, Lecture notes 6, random effects modeling pages 1-3, also brief discussion of ADAP, and discussion of residual analysis in lab 9. 
• Lecture 19 Monday 2024_06_03. Finished lectures notes 6, pages 3-33. 
• Lecture 20 Wednesday 2024_06_05. Lecture notes 7, Random effects models, Missing Data, pages 1 to 7. 

Labs.

• Lab 01 Wednesday 2024_04_03. Introduction, Data Management
• Lab 02 Wednesday 2024_04_10. Graphics
• Lab 03 Wednesday 2024_04_17. Simple Analyses
• Lab 04 Wednesday 2024_04_24. Fitting Longitudinal Models
• Lab 05 Wednesday 2024_05_01. Covariance Models
• Lab 06 Wednesday 2024_05_08. Clustered Data
• Lab 07 Wednesday 2024_05_13. Discrete Data
• Lab 08 Wednesday 2024_05_22. Using SAS to fit covariance models (tentative)
• Lab 09 Wednesday 2024_05_29. Residuals
• Lab 10 Wednesday 2024_06_05. No lab, informal office hour.

SAS Documentation

• Code examples for all of Modeling Longitudinal Data!
• On line Proc Mixed documentation from SAS Institute
• SAS Glimmix Procedure and Documentation