Biostat 236 | Rob Weiss | Biostat Faculty

Biostatistics 236 Modeling Longitudinal Data

Fall 2024 - Robert Weiss

News & Announcements:

Final Data Analysis Project (FDAP) Abstract Comments.
Lab 8 is a Bayesian Logistic Random Intercept Model for the Kenyan School Lunch Morbidity Data.
- You will need to download and install JAGS, ideally before the lab. See instructions at the top of the R code. Note that I have a windows laptop, and am not sure how instructions might differ for Apple.
Quick summary of useful graphics
Data Analysis Project Comments and General Advice.
Variance Covariance Choices.
(For those who are interested) SAS Studio Introduction (word). Not part of the course.
2024F Syllabus has been posted.

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.

2024F Class Schedule, Email Addresses, and Office Hours:

For Zoom links see Syllabus

Event	Time	Day	Room
Lecture	1:00 -- 2:50	Thursday	Factor 3-648
Lecture	1:00 -- 1:50	Tuesday	Factor 3-648
Computer Lab	2:00 - 2:50	Tuesday	Factor 3-648
Prof	Robert Weiss	robweiss@ucla.edu
Office Hours	Monday	3:00 -- 3:45	zoom
Office Hour	Wednesday	10:00 -- 10:45	zoom
TA	Carolyn Winskill	cwinskill@g.ucla.edu	TA TBA
Office Hours	6:00 - 6:50pm	Tuesday	zoom
	9:00 - 9:50	Friday	zoom

Syllabus and Textbook

2024F Syllabus has been posted. Includes 2024F HW due dates and 2024F 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

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

Homeworks (Original LaTeX)

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
- GGplot code for multiple outcomes (edited after lab 2 to prevent errors)
- Output
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
Lab 6 Hierarchical Models BSI data and Weight Loss Data
Lab 7 Longitudinal Models for Discrete Data
- Spring 2024 (only) Meets on Monday instead of Wednesday
- Handout
- Selected Output
- R Commands
Lab 8 Intro to a Bayesian Analysis to fit a Logistic Random Effects Model
<Defunct> Old 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)
<Defunct> Old 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)
Lab 3 Simple Analyses
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

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 Thursday 2024_09_26. Syllabus. Lecture notes 1 intro pages 1-17.
Lecture 02 Tuesday 2024_10_01. Pediatric Pain Data, Kenya School Lunch Cognitive data. Lecture notes 1 page 17.
Lecture 03 Thursday 2024_10_03. Lecture notes 1 pages 18- 28. Lecture notes 2 Graphics pages 1-34.
Lecture 04 Tuesday 2024_10_08. Lecture notes 2 Graphics pages 35-49. Lab 2.
Lecture 05 Thursday 2024_10_10. Lecture notes 3 pages 1-21, talked about lab 3.
Lecture 06 Tuesday 2024_10_15. Lecture notes 3, multivariate normal model pages 21-34.
Lecture 07 Thursday 2024_10_17. Lecture notes 3 pages 35-45. Lab 4 discussion of annotated output for lab 4.
Lecture 08 Tuesday 2024_10_22. Lecture notes 3 pages 46-48. Lecture notes 4 pages 1-20.
Lecture 09 Thursday 2024_10_24. Lecture notes 4 Covariates pages 20-36. Lab 5 output.
Lecture 10 Tuesday 2024_10_29. Lecture notes 4 Covariates pages 37-53.
Lecture 11 Thursday 2024_10_31. Lecture notes 4 Covariates pages 54-66 (top half). Lab 6.
Lecture 12 Tuesday 2024_11_05. Lecture notes 4 Covariates 66-69 (end). Lecture notes 5 Covariance Modeling 1-13.
Lecture 13 Thursday 2024_11_07. Lecture notes 8 pages 14-17 on Logistic Random Effects Model. Lab 7 output. Lecture notes 5 Covariance models pages 14-30.
Lecture 14 Tuesday 2024_11_12. Finished lecture notes 5 Covariance Models pages 31-42. Started lecture notes 6 random effects models pages 1 to 5(top).
Lecture 15 Thursday 2024_11_14. Lab 8 discussion, fitting a Bayesian model to discrete longitudinal data. Lecture notes 6 random effects models, pages 5 to 15.
Lecture 16 Tuesday 2024_11_19. Comments on abstracts/projects (see announcements up above). Lecture notes 6 random effects modeling, pages 16 to top of page 23 top.
Lecture 17 Thursday 2024_11_21.
Lecture 18 Tuesday 2024_11_26.Lecture notes 5 covariance modeling, pages 41-42, Lecture notes 6, random effects modeling pages 1-8.
Lecture 19 Tuesday 2024_12_03. Finished lectures notes 6, pages 8-33.
Lecture 20 Thursday 2024_12_05. Lecture notes 9, Bivariate Longitudinal Data, pages 1 to 17.

Labs.

Lab 01 Tuesday 2024_10_01. Introduction, Data Management
Lab 02 Tuesday 2024_10_08. Graphics
Lab 03 Tuesday 2024_10_15. Simple Analyses
Lab 04 Tuesday 2024_10_22. Fitting Longitudinal Models
Lab 05 Tuesday 2024_10_29. Covariance Models
Lab 06 Tuesday 2024_11_05. Clustered Data
Lab 07 Tuesday 2024_11_12. Discrete Data
Lab 08 Tuesday 2024_11_19. Using Bayesian methods to fit longitudinal discrete data
Lab 09 Tuesday 2024_11_26. No lab, informal office hour.
Lab 10 Tuesday 2024_12_03. No lab, informal office hour.

SAS Documentation