Longitudinal analysis: An introduction to concepts, methods & software

When

Date Time
Tuesday 3rd September 2024 13:30 – 17:00 (CET)
Wednesday 4th September 2024 13:30 – 17:00 (CET)
Thursday 5th September 2024 13:30 – 17:00 (CET)

 


Where

This is an online course consisting of three sessions (lectures and computer labs) spread out over three days.


Course description

Longitudinal data refers to data that is repeatedly collected over time for each study subject. Compared to cross-sectional studies, longitudinal studies allow us to study changes over time, e.g., to quantify the change in the longitudinal response variable and identify factors that influence change.

This course will provide an overview of concepts, methods, and software for the analysis of longitudinal data, with a strong focus on mixed-effects models, and runs in three sessions over three days.

Session 1: Overview of basic concepts in longitudinal data analysis, including notation. Introducing approaches for describing longitudinal data. Review of linear regression models, including interpretation of statistical interaction terms.

Session 2: Introduction to linear mixed-effects models and comparison with linear regression models. Introduction to random intercept, random intercept and slope models. We will also discuss model-building strategies, including approaches for selecting the random effects structure, the variance-covariance matrix structure, and the functional form of time.

Session 3: Introduction to post-estimation, model-based predictions that can be obtained after fitting linear mixed-effects models, including residuals. Both population-level and subject-specific predictions will be discussed and compared

Faculty

Alessandro Gasparini, PhD
Senior Biostatistician and Software Developer

What to expect

The course instructor has formal training in statistics and biostatistics and experience in developing statistical methods and applied clinical research. Each session will consist of a mix of  statistical concepts and applied examples, with emphasis on interpretation and practical aspects (such as computing). Course material, including lecture slides, tutorial exercises and solutions, and additional references for further reading will be provided.

Computing

The primary software for the course will be Stata, with support for Stata version 14 or higher.

Who should attend?

Epidemiologists, statisticians, physicians, public health specialists or anyone with an interest in methods for studying longitudinal data. The course focuses on the analysis of longitudinal data in medical settings, but we welcome participants with interests in other areas; the methods we teach can be applied to other research areas as well.

Expected prior knowledge

We expect participants to possess basic knowledge of the fundamentals of epidemiology and biostatistics and be comfortable fitting and interpreting statistical models in epidemiology (e.g.,  linear regression, logistic regression, Poisson regression, or Cox regression). Prior basic knowledge of Stata will be assumed.


Register your place

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