Advanced Statistics: Statistical Modelling

Overview

While the statistical models and tools presented in an introductory statistics course (such as linear regression) can be used to answer a wide range of questions in life sciences, many types of data cannot be analyzed using these simple approaches.

During this course, we will discuss statistical models and techniques beyond classical linear modelling. Following a brief review of the basics of linear regression, we will dive into more advanced topics, such as generalized and mixed-effects linear models. We will further discuss the application of mixed-effects linear models in analyzing longitudinal data. Finally, in an attempt to move beyond linearity, we will explore extensions of linear models, such as polynomial regression, splines, local regression, and generalized additive models. Throughout the course, the emphasis will be put on concrete applications in clinical and biological data analysis using real world examples.

Audience

This course is intended for life scientists who already use the R programming language and have some basic knowledge of statistics (including statistical tests, correlation, and linear models).

Learning outcomes

At the end of this course, participants will be able to:

identify the appropriate model to analyze a dataset;
fit the chosen model using R;
assess the fit of the model, as well as its limitations.

Knowledge / competencies

The course is intended for people already familiar with basic statistics and R. Participants must be comfortable with topics such as hypothesis testing, correlation and linear models, and must have a prior knowledge of the "R" language and environment for statistical computing and graphics. Participants who have already followed the SIB course "Introduction to statistics with R" or an equivalent course, and have used its content in practice should fit this prerequisite.

Before applying to this course, please self-assess your knowledge in stats and R to make sure this course is right for you. Here are 2 quizzes:

Technical

You are required to have your own laptop, with at least 4 Gb of RAM, "R" v4.2.0 and "RStudio" 2022.02.2-485 software installed. More information about the packages needed will be provided in due time.

Brief course programme

Monday: simple and multiple linear regression (theory, diagnostics, and model selection)
Tuesday: generalized linear models (binary data, proportions, and counts)
Wednesday: mixed-effects linear models, longitudinal data analysis
Thursday: smoothing and generalized additive models

Application

The registration fees for academics are 400 CHF and 2000 CHF for for-profit companies.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications will close as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to 21/08/2023. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

This course will be held at the University of Lausanne.

The course will start at 9:00 and end around 17:00. Precise information will be provided to the participants in due time.

Additional information

Coordination: Valeria Di Cola, SIB training group.

We will recommend 1 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.