Instructor: Dr. Ana-Maria Staicu[last name pronounced as "styku"]

 Contact Information Office: 5242 SAS Hall / Phone: 515-0644 Email: ana-maria_staicu [at]  ncsu [dot] edu Office Hours Tuesday 3:00AM - 3:50PMFriday 4-5PM

Teaching Assistant: Xiaoshan Li
Office Hours: Monday 11-12PM, Thursday 10-11AM. Room SAS Hall 1101
Email: xli12 [at] ncsu [dot] edu

Statistical software:
SAS [on-line documentation for version 9.1.3 ].
R will also be used; R is freely available at http://www.r-project.org/.
Intro to R. More about R: R Lab [Courtesy of Charlie Smith]

Announcements

(NEW, March 17)

[M, Jan-6-2013] First Day of Class
[M, Jan-20-2013] Martin Luther King [NO CLASS]
[M, W March 10,14-2013] Spring Break [NO CLASS]
[M, March17] Class cancelled
[W, April-23-2013] Last Day of Class

Lecture Notes (tentative)

 Chapter 1: Introduction, class organization, grading, course overview Chapter 2: Matrix review (individual study).  Chapter 3: Review: Multivariate normal. Conditional expectation.                  Review: Linear regression: OLS and WLS estimation Introduction to modeling longitudinal data Chapter 4.: Balanced design arising from single population.                   Mean response. Variation about the mean. Sources of correlation. Common correlation models.                  Graphical summaries Chapter      Balanced design arising from two or more populations                   SAS code: Exploratory tools for mean and covariance (Proc CORR, DISCRIM)                  R code: Basic exploratory tools for mean and covariance (scatterplot matrix) Univariate repeated measures ANOVA [classical methods] Chapter 5.1: Introduction. Statistical model (Split Plot) for balanced, regular design. Chapter 5.2: Questions of interest and Statistical Hypotheses Chapter 5.3: ANalysis Of VAriance                 R code: Split -Plot designs                 SAS code: Proc GLM  with random/repeated statement                 Additional reference:  Proc GLM Chapter 5.4: Violation of covariance matrix assumption  Chapter 5.5: Specialized within-unit hypotheses and tests Multivariate ANOVA (Chapter 6, individual study)Limitations of the classical methods ( Chapter 7) General linear models for longitudinal data Chapter 8.1: Introduction Chapter 8.2: General models for longitudinal data Chapter 8.3: Modeling the covariance Chapter 8.4: Mean regression parameters estimation: Maximum Likelihood and Restricted Maximum Likelihood                 SAS code: Proc REG (OLS estimation) Chapter 8.5: Mean regression parameters inference. Model selection approaches (LRT, AIC, BIC) Chapter 8.6: Final Remarks: main features and limitations                SAS code: Proc MIXED/ repeated statement                 SAS resource: Proc MIXED                R resource: library NLME - GLS() Random Coefficient Model  (Growth Curve Model). Linear Mixed Model Chapter 9.1: Introduction Chapter 9.2: Random coefficient model Chapter 9.3: Inference on mean regression parameters and covariance parameters                 SAS code: Proc MIXED repeated/random statement                 R resource: library NLME - LME() Chapter 10.1: General Linear mixed effects model Chapter 10.2:  Inference on the regression parameters and covariance parameters                 SAS code: Proc MIXED repeated/random statement                 R code: library NLME - LME()  Chapter 10.3: Best Linear Unbiased Prediction (BLUP) for subjects effects and individual trajectories                 SAS code: Proc MIXED prediction Chapter 10.4: Comparing nested models for the covariance: testing whether an effect is random                 SAS code: Proc MIXED testing  [Weigth lifting study]  PressData Chapter 10.4: Accounting for covariate information Generalized Linear Models. Scalare responses / Longitudinal data Chapter 11.1: Introduction Chapter 11.2: Three-part specification of GLM Chapter 11.3: Estimation and inference for regression parameter                      Iterative re-weighted least squares (IRWLS) Chapter 11.4: Illustrative examples: Logistic Regression; Log llinear regression.                      SAS code: Proc GENMOD [Myocard Infarction]                      R code: GLM                       SAS code: Proc GENMOD [Horsekicks]                         SAS code: Proc GENMOD [Clotting data]                SAS code: Proc MIXED testing [Weigth lifting study] Chapter 12.1: Introduction Chapter 12.2: Specification of marginal models Chapter 12.3: Estimation and inference for marginal models                   Generalized Estimating Equations                    SAS code: Proc GENMOD [Epileptic seizures]                    SAS code: Proc GENMOD [Respiratory illness] Chapter 12.4: Generalized linear mixed models.                    R code:  LME4 package                    SAS code: Proc NLMIXED [Respiratory illness] Chapter 12.5: Population averaged vs subject specific approaches Chapter 12.6  Illustrations.                    SAS code: Proc NLMIXED [Epileptic seizures]

 Homework 1 (Due January 22, 2014)   Solution. Soln file Homework 2 (Due February 10, 2014) Solution. Soln file Homework 3 (Due February 26, 2014) SolutionHomework 4 (Due March 26 2014)     Soln fileHomework 5 (Due April 23 2014)     Soln file Homework 5 (Due April 11, 2013)       Solution: HW5 soln. Additional files

Data sets

Courses Objectives

To introduce students to statistical models and methods for the analysis of longitudinal data, i.e. data collected
repeatedly on individuals (humans, animals, plants, samples, etc) over time (or other conditions).

Prerequisite
ST 512, Experimental Statistics for Biological Sciences II, or equivalent. Thus, students should be familiar with basic notions of probability, random variables, and statistical inference, analysis of variance, and (multiple) linear regression. Familiarity with matrix algebra is also useful. We will review matrix algebra at the beginning of the course and make considerable use of matrix notation and operations throughout. ST 512 involves the use of the SAS (Statistical Analysis System) software package; thus, students are expected to have had some exposure to the use of SAS. The course is meant to be accessible both to non-majors and majors. The underlying mathematical theory will not be stressed, and the main focus will be on concepts and applications. Please see the instructor if you have questions about the suitability of your background.

Required Text
Lecture notes prepared by Marie Davidian will be used. These may be purchased at the Sir Speedy across the street from Patterson on Hillsborough. You should obtain a copy.