syllabus-PubH8452 - Course Syllabus PubH 8452 Advanced...

Info icon This preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
Course Syllabus 1 PubH 8452 Advanced Longitudinal Data Analysis Fall 2011 Credits: 3 Meeting Days: Monday, Wednesday and Friday Meeting Time: 11:15AM-12:05PM MWF (09/06/2011-12/14/2011) Meeting Place: Moos Health Sci Tower 1-440, TCEASTBANK Instructor: Dr. Xianghua Luo Office Address: 420 Delaware St. SE, Room A455 Office Phone: 612-6242158 Fax: 612-6260660 E-mail: [email protected] Office Hours: Monday 1:00PM-2:00PM or by appointment Course Website: http://www.biostat.umn.edu/~xianghua/8452/index.htm I. Course Description Methods of inference for correlated outcome variables, with a special emphasis on repeated measurements in medical studies. Linear/nonlinear models with either normal or non-normal error structures. Random effects. Transitional/marginal models. II. Course Prerequisites Theory of statistical inference (estimation and testing, asymptotics) at or above the level of Stat 8101-2. Stat 8111-2 is recommended. Linear models (linear algebra, least square, multivariate normal distribution) (Stat 8311 required). Familiarity with a statistical software package to carry out the computation (including data analysis and simulation). R is highly recommended and will be used throughout by the instructor. Or permission of the Instructor. III. Course Goals and Objectives After taking the course, the students are expected to: understand the theory, assumptions and properties of various statistical methods for the analysis of longitudinal data. be able to carry out the appropriate analyses (including exploratory) of longitudinal data using suitable statistical software and present the results.
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
2 IV. Methods of Instruction and Work Expectations Mostly there will be lectures. The notes will be distributed in the classroom prior to the lecture and will also be available on the course website afterward. Additional reading materials will be distributed as needed. Work expectations: Students are expected to attend class, participate in class discussion, and complete all assigned homework, presentation, and project. V. Course Text and Readings Textbook (Required) Diggle, Heagerty, Liang and Zeger (2002), Analysis of Longitudinal Data, 2nd Edition, Oxford University Press. Readings (Optional) Hedeker and Gibbons (2006). Longitudinal Data Analysis. Johns Wiley & Sons, Inc. ISBN-10: 0-471- 42027-1. ISBN-13: 978-0-471-42027-9. Fitzmaurice, Laird and Ware (2004). Applied Longitudinal Analysis. John Wiley and Sons. ISBN: 0-471- 21487-6. McCullaph and Nelder (1989). Generalized linear models. 2nd Edition, Chapman and Hall. ISBN: 0-412- 31760-5.
Image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern