Statistical Methods for Longitudinal Research

Office Hours after class (plus additional TBA)

Course web page: http://rogosateaching.com/stat222//

To see full course materials from Autumn 2017 go here

Registrar's informationSTATS 222 (Same as EDUC 351A): Statistical Methods for Longitudinal Research Units: 2-3 Lecture Th 3:00PM - 5:15PM Sequoia 200 Rogosa Office Hour: 5:15 - 5:50PM, Sequoia 224 Grading Basis: Letter or Credit/No Credit Course Description: STATS 222: Statistical Methods for Longitudinal Research (EDUC 351A) Research designs and statistical procedures for time-ordered (repeated-measures) data. The analysis of longitudinal panel data is central to empirical research on learning, development, aging, and the effects of interventions. Topics include: measurement of change, growth curve models, analysis of durations including survival analysis, experimental and non-experimental group comparisons, reciprocal effects, stability. See http://rogosateaching.com/stat222/. Prerequisite: intermediate statistical methods Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit Instructors: Rogosa, D. (PI)

Week 1. Course Overview, Longitudinal Research; Analyses of Individual Histories and Growth Trajectories

Week 2. Introduction to Data Analysis Methods for assessing Individual Change for Collections of Growth Curves (mixed-effects models)

Week 3. Analysis of Collections of growth curves: linear, generalized linear and non-linear mixed-effects models

Week 4. Special case of time-1, time-2 data; Traditional measurement of change for individuals and group comparisons

Week 5. Assessing Group Growth and Comparing Treatments: Traditional Repeated Measures Analysis of Variance and Linear Mixed-effects Models

Week 6. Comparing group growth continued: Power calculations, Cohort Designs, Cross-over Designs, Methods for missing data, Observational studies.

Week 7. Analysis of Durations: Introduction to Survival Analysis and Event History Analysis

Weeks 8-9. Further topics in analysis of durations: Diagnostics and model modification; Interval censoring, Time-dependence, Recurrent Events, Frailty Models, Behavioral Observations and Series of Events (renewal processes)

Dead Week. Assorted Special Topics (enrichment) and Overflow (weeks 1-8): Assessments of Stability (including Tracking), Reciprocal Effects, (mis)Applications of Structural Equation Models, Longitudinal Network Analysis

1. Garrett M. Fitzmaurice Nan M. Laird James H. Ware Applied Longitudinal Analysis (Wiley Series in Probability and Statistics; 2nd ed 2011)

Text Website second edition website Text lecture slides [note: Harvard links broken in August, now (9/21) fine]

2. Judith D. Singer and John B. Willett . Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence New York: Oxford University Press, March, 2003.

Text web page Text data examples at UCLA IDRE Powerpoint presentations good gentle intro to modelling collections of growth curves (and survival analysis) is Willett and Singer (1998)

3. Douglas M. Bates. lme4: Mixed-effects modeling with R February 17, 2010 Springer (chapters). There was [An merged version of Bates book: lme4: Mixed-effects modeling with R January 11, 2010] but link broken at this time

Manual for R-package lme4 and mlmRev, Bates-Pinheiro book datasets.

Additional Doug Bates materials. Collection of all Doug Bates lme4 talks Mixed models in R using the lme4 package Part 2: Longitudinal data, modeling interactions Douglas Bates 8th International Amsterdam Conference on Multilevel Analysis 2011-03-16 another version

Original Bates-Pinheiro text (2000). Mixed-Effects Models in S and S-PLUS (Stanford access). Appendix C has non-linear regression models.

Fitting linear mixed-effects models using lme4,

4. A handbook of statistical analyses using R (second edition). Brian Everitt, Torsten Hothorn CRC Press, Index of book chapters Stanford access Longitudinal chapters: Chap11 Chap12 Chap13. Data sets etc Package 'HSAUR2' August 2014, Title A Handbook of Statistical Analyses Using R (2nd Edition)

There is now a third edition of HSAUR, but full text not yet available in crcnetbase.com. CRAN HSAUR3 page with Vignettes (chapter pieces) and data in reference manual

5. Peter Diggle , Patrick Heagerty, Kung-Yee Liang , Scott Zeger. Analysis of Longitudinal Data 2nd Ed, 2002

Amazon page Peter Diggle home page Book data sets

A Short Course in Longitudinal Data Analysis Peter J Diggle, Nicola Reeve, Michelle Stanton (School of Health and Medicine, Lancaster University), June 2011 earlier version associated exercises: Lab 1 Lab2 Lab3

6. Longitudinal and Panel Data: Analysis and Applications for the Social Sciences by Edward W. Frees (2004). Full book available and book data and programs (mostly SAS).

7. Growth Curve Analysis and Visualization Using R. Daniel Mirman Chapman and Hall/CRC 2014 Print ISBN: 978-1-4665-8432-7 Stanford Access Mirman web page (including data links).

8.

9. Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. Springer Series in Statistics. New-York: Springer. Extended presentation: Introduction to Longitudinal Data Analysis A shorter exposition: Methods for Analyzing Continuous, Discrete, and Incomplete Longitudinal Data

10. Survival analysis Rupert G. Miller. Available as Stanford Tech Report

11. Event History Analysis with R (Stanford access). Goran Brostrom CRC Press 2012. R-package

12. John D. Kalbfleisch , Ross L. Prentice The Statistical Analysis of Failure Time Data 2nd Ed

Amazon page online from Wiley

13. Advanced survival analysis topics.

Interval-Censored Time-to-Event Data Methods and Applications Chapman and Hall/CRC 2012 (esp Chap 14--glrt).

Recurrent Events: Chapter 9 of Kalbfleisch and Prentice (2nd edition), "Modeling and Analysis of Recurrent Event Data".

Cook, R. J. and Lawless, J. F. (2007). The Statistical Analysis of Recurrent Events. (Stanford access) Springer, New. York.

Joint Models for Longitudinal and Time-to-Event Data. With Applications in R. Dimitris Rizopoulos. Chapman and Hall/CRC 2012(Stanford access) Book website

Additional Specialized Resources

Harvey Goldstein. The Design and Analysis of Longitudinal Studies: Their Role in the Measurment of Change (1979). Elsevier

Amazon page Goldstein Chap 6 Repeated measures data Multilevel Statistical Models by Harvey Goldstein with data sets

David Roxbee Cox, Peter A. W. Lewis The statistical analysis of series of events. Chapman and Hall, 1966

Google books Poisson process computing program

David J Bartholomew. Stochastic Models for Social Processes, Chichester 3rd edition: John Wiley and Sons.

David J Bartholomew web page

Stat222/Ed351A is listed as Letter or Credit/No Credit grading (Stat MS students should check whether S/NC is a viable option for their degree program.)

Grading (for the 2-unit base) will be based on two components:

Each week I will post a few exercises for that week's content--towards the end of the qtr I'll identify a subset of those exercises to be turned in.

During the Autumn qtr exam period we will have an in-class (all materials available, "open" everything) exam.

My reading of the Registrar's chart indicates Tuesday, December 11, 2018 3:30-6:30 p.m. Location: Sequoia 200 (Statistics).

see Class Calendar for details

The Registrar requires clear identification of the requirements for incremental units. The additional requirement for a 3-unit registration (the one unit above 2-units) is satisfied by a student presentation: a mini-lecture, approximately 15 minutes with handout. These are done with Rogosa in Sequoia 224, which has worked out well. Good topics would include empirical longitudinal research, such as a data set or set of studies you are involved with, or an extension of class lecture topics such as preparing an additional data analysis example or a report on some technical readings. Discussion with Rogosa is encouraged.

Course Problem Set 2018 to be posted xxx

Cumulative Collection of Course Handounts 2018 to be posted Dec 2018

Class presentation will be in, and students are encouraged to use, R (occasionally, some references to SAS and Mathematica).

Current version of R is R version 3.5.1 (Feather Spray) released 2018-07-02.

For references and software: The R Project for Statistical Computing Closest download mirror is Berkeley

The CRAN Task View: Statistics for the Social Sciences provides an overview of some relevant R packages. Also the new CRAN Task View: Psychometric Models and Methods and CRAN Task View: Survival Analysis and CRAN Task View: Computational Econometrics.

A good R-primer on various applications (repeated measures and lots else). Notes on the use of R for psychology experiments and questionnaires Jonathan Baron, Yuelin Li. Another version

A Stat209 text, Data analysis and graphics using R (2007) J. Maindonald and J. Braun, Cambridge 2nd edition 2007. 3rd edition 2010 has available a short version in CRAN .

According to Peter Diggle: "The best resource for R that I have found is Karl Broman's Introduction to R page."

1. Now they tell us.... Daily aspirin may be harmful for healthy older adults, large study finds Publication: Effect of Aspirin on Disability-free Survival in the Healthy Elderly and related articles. The New England Journal of Medicine nejm.org September 2018.

2.

A. Initial meet-and-greet. Class logistics and longitudinal research overview

B. Examples, illustrations for longitudinal research overview, taken from course resources above:

Laird,Ware (#1) slides 1-16; Diggle (#5) slides 4-14, 22-28 Verbeke (#9) slides from Ch 2 and Sec3.3

C. Data Analysis Examples of Model Fitting for Individual Trajectories and Histories.

ascii version of class handout annotated version pdf version with plots datasets

Starting up R-addendum: installing packages and obtaining data (sleepstudy in lme4)

For Count Data (glm) example. Link functions for generalized linear mixed models (GLMMs), Bates slides (pdf pages 11-18)

AIDS in Belgium example, (from Simon Wood) single trajectory, count data using glm. Rogosa R session for aids data

aditional expositions of AIDS data, Poisson regression: Duke Kentucky

A

Non-linear models, esp logistic. From week 1, also week 3 Self-Starting Logistic model SSlogis help page, do

Trend in Proportions: College fund raising example prop.trend.test help page

Trend in proportions, group growth, Cochran-Armitage test. Expository paper: G. Salanti and K. Ulm (2003): Tests for Trend in Binary Response (SU access)

1. For the straight-line (constant rate of change) fit example to subj 372 in the sleepstudy data. Obtain a confidence interval for the rate of change from the OLS fit. Now compare the OLS fit with day-to-day differences. Under the constant rate of change model these 9 day to day differences also estimate the rate of change. Obtain a estimate of the mean and a confidence interval for rate of change from these first differences. Compare with OLS results.

For reference, Self-Starting Logistic model SSlogis help page, do

North Carolina, female math performance (also in Rogosa-Saner) North Carolina data (wide format); NC data (long)

For that female, what is the rate of improvement over grades 1 through 8? Compare the observed improvement for grades 1 through 8 (the

Seperately, consider three observations at taken at equally spaced time intervals: What is a simple expression for the OLS slope (rate of change)?