Legend: Boston Convention & Exhibition Center = CC, Westin Boston Waterfront = W, Seaport Boston Hotel = S
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_11C | Sun, 8/3/2014, 8:30 AM - 5:00 PM | CC-159 | |
| Applied Longitudinal Analysis — Professional Development Continuing Education Course | |||
| ASA , Biometrics Section | |||
| The goal of this course is to provide a broad introduction to statistical methods for analyzing longitudinal data. The main emphasis is on the practical rather than the theoretical aspects of longitudinal analysis. The course begins with a review of established methods for longitudinal data analysis when the response of interest is continuous. A general introduction to linear mixed effects models for continuous responses is presented. Next, we discuss how smoothing and semiparametric regression allow greater flexibility for the form of the relationship between the mean response and covariates. We demonstrate how the mixed model representation of penalized splines makes this extension straightforward. When the response of interest is categorical (e.g., binary or count data), two main extensions of generalized linear models to longitudinal data have been proposed: "marginal models" and "generalized linear mixed models". While both classes of models account for the within-subject correlation among the repeated measures, they differ in approach. In this course we highlight the main distinctions between these two types of models and discuss the types of scientific questions addressed by each. Prerequisite Knowledge: Attendees should have a strong background in linear regression and some minimal exposure to generalized linear models (e.g., logistic regression). | |||
| Instructor(s): Garrett Fitzmaurice, Harvard, Nan Laird, Harvard | |||
2014 JSM Online Program Home
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