Advanced Statistics: Statistical Modeling

We are sorry but registration for this course is now closed as there is a long waiting list.

Updated 21 July 2020: This course will be in face-to-face format complying with the rules associated with the current COVID-19 situation (distance and hygiene). In the event of new restrictions from the Swiss Government, this course will be streamed and the participants will be informed.

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 can not 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 objectives

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

Brief course program

• 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

Prerequisites

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 for biologists" and have used its content in practice should fit this prerequisite.

Technical

Participants must bring a laptop with at least 4 Gb of RAM and the "R" and "RStudio" software installed. More information about the packages needed will be provided before the course.

Application

The registration fees are 240 CHF for academics and 1200 CHF for for-profit companies. This includes a social dinner on Tuesday 18 August.

Deadline for registration and free-of-charge cancellation is set to 3 August 2020. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to this and other general conditions, available here.

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.

Venue and time

University of Lausanne, Amphipôle building

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: Monique Zahn

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.

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.