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---+++ <span style="color: #4682b4; line-height: 17px;">BIOS 346: Advanced Regression Analysis II (Generalized Linear and Longitudinal Models)</span> <span style="color: #000000;"><span style="line-height: normal;"><span style="color: #4682b4;">Instructor</span></span></span> * Jonathan Schildcrout, !PhD, Associate Professor of Biostatistics * jonathan.schildcrout@vanderbilt.edu * 11117: 2525 West End Ave Building * 322-1357 <span style="color: #4682b4;">Teaching Assistant</span> * David Schlueter, MS <span style="color: #4682b4;">Schedule</span> * Lectures: Tuesday and Thursday 9:00-10:30, Small Classroom, 11th floor, 2525 WEA * Lab: Friday 9:00-10:00, Small Classroom, 11th floor, 2525 WEA * Office hours: * Jonathan: 10:30am-11:30pm Tuesdays and Thursdays and by appointment. <span style="color: #4682b4;">Textbooks</span> * Required * Diggle PJ, Heagerty P, Liang K-Y, Zeger SL. _Analysis of Longitudinal Data_, 2nd Edition. Oxford University Press, 2002. * Recommended * Verbeke G, Molenberghs G. _Linear Mixed Models for Longitudinal Data_. Springer Series in Statistics, 20. * Molenberghs G, Verbeke G. _Models for Discrete Longitudinal Data_. Springer Series in Statistics, 2010. <span style="color: #4682b4;">Course Objectives</span> This course will cover advanced topics in generalized linear models and it will extend general and generalized linear models to the analysis of correlated or longitudinal response data. The vast majority of the course will focus on parametric and semi-parametric regression methods. At the end of the course, students should: * Understand the theoretical framework for longitudinal data analysis * Understand statistical properties of longitudinal data analysis methods (e.g., GLMs, mixed effects models, and estimating equations estimators) * Be able to apply appropriate techniques for inferences in GLM and longitudinal data settings, examine the validity of the approach, and be able to interpret the results. * Be reasonably familiar with current research topics in longitudinal data methodology and applications. <span style="color: #4682b4;">Course Outline</span> This course with be comprised of six modules covering mostly distinct topics in generalized linear models and longitudinal data analysis * Introductory topics in longitudinal data analysis (LDA) * Linear mixed-effects models * Marginal models and estimating equation * Generalized linear mixed-effects models * Advanced LDA topics <span style="color: #d1400e; line-height: 17px;"><span style="color: #4682b4;">Other information</span></span> * Homework needs to be turned in beginning of class on the due date. Often, we will discuss the homework during the Friday discussion section, so it might be helpful to photocopy your homework so you can refer to it. * Students are encouraged to work together on homework problems, but they must turn in their own write-ups. * You will often need a laptop for the discussion section on Fridays, but you should not bring them on lecture days <span style="color: #d1400e; line-height: 17px;"><span style="color: #4682b4;">Grading</span></span> * [[Bios346HwGuidelines][Homework]]: 25% * Take-home Midterm Exam: 30% * Take-home Final Exam: 40% * Class participation: 5% -- Main.JonathanSchildcrout - 02 Jan 2013
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Topic revision: r27 - 16 Jan 2015,
JonathanSchildcrout
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