BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260616T134924Z UID:1c9768e0-6188-4e84-83e9-f593877bbeb3 DTSTART:20250901T090000Z DTEND:20250904T170000Z DESCRIPTION:# Overview\nWhile 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.\n\nDuring this course\, we will discuss statistical models and techniques beyond classical linear modeling. Following a brief review of the basics of simple and multiple l inear regression\, we will dive into more advanced topics\, such as genera lized and mixed-effects linear models. We will further discuss the applica tion of mixed-effects linear models in analyzing longitudinal data. In an attempt to move beyond linearity\, we will explore extensions of linear mo dels\, such as polynomial regression\, splines\, local regression\, and ge neralized additive models or logistic regressions in order to model for ex ample binomial data. On the last day\, we will dive into model performance s\, training and test sets\, regularization and cross validation. These ar e the foundations of machine learning and artificial intelligence. Through out the course\, the emphasis will be put on concrete applications in clin ical and biological data analysis using real world examples.\n\n# Audience \nThis course is intended for life scientists who already use the R progra mming language and have some basic knowledge of statistics (including stat istical tests\, correlation\, and linear models).\n\n# Learning outcomes\n At the end of this course\, participants will be able to:\n* identify the appropriate model to analyze a dataset\n* fit the chosen model using R\n * assess the fit of the model\, as well as its limitations\n* perform re gularization or cross-validation\n\n### ***Knowledge / competencies***\nTh e course is intended for people already **familiar with basic statistics a nd R**. Participants must be comfortable with topics such as hypothesis te sting\, 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 t o statistics with R"](https://www.sib.swiss/training/course/2021-02-intro- stats) or an equivalent course\, and have used its content in practice sho uld fit this prerequisite. \n\n**Before applying to this course\, please self-assess your knowledge in stats and R to make sure this course is righ t for you. Here are 2 quizzes:** \n- [Quiz: Introduction to Statistics ]( https://gohighbrow.com/quiz-introduction-to-statistics/)\n \n- ["Introduct ion to R" self-assessment for the advanced statistics course](https://docs .google.com/forms/d/e/1FAIpQLSfXCnmLha0Ks4ZZZ42G_5MyIbGi-JhPayuHZ_P2jdXZEt Xdqg/viewform)\n \n\n\n### ***Technical***\nYou are required to have **you r own laptop\, with at least 4 Gb of RAM\, as well as [R v. 4.5.0](https:/ /cran.r-project.org/) and [RStudio 2025.05.1-513](https://www.rstudio.com/ products/rstudio/download/#download) software installed**. More informatio n about the packages needed will be provided in due time. \n\n# Brief cour se programme\n* Monday: simple and multiple linear regression (theory\, d iagnostics\, and model selection)\n* Tuesday: generalized linear models ( binary data\, proportions\, and counts)\n* Wednesday: mixed-effects linea r models\, longitudinal data analysis\n* Thursday: Model Performance\, Se nsitivity-Specificity ROC\, Regularization\, k-fold Cross validation and  Leave-one-out method (L1O)\n\n# Application\nThe registration fees for aca demics are **400 CHF** and **2000 CHF** for for-profit companies.\n\nYou w ill be informed by email of your registration confirmation. Upon reception of the confirmation email\, participants will be asked to confirm attenda nce by paying the fees within 5 days.\n\n\nApplications will close as soon as the places will be filled up. Deadline for free-of-charge cancellation is set to *18/08/2025*. Cancellation after this date will not be reimburs ed. Please note that participation in SIB courses is subject to our [gener al conditions](https://www.sib.swiss/training/terms-and-conditions).\n\n# Venue and Time\nThis course will be held at the University of Lausanne.\n\ nThe course will start at 9:00 and end around 17:00. Precise information w ill be provided to the participants in due time.\n\n\n# Additional inform ation\nCoordination: Monique Zahn\, SIB Training group.\n\nWe will recomme nd 1 ECTS credits for this course (given a passed exam at the end of the c ourse).\n\nYou 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](https://lists.sib.swiss/mailman/listinfo/ courses).\n\nPlease note that participation in SIB courses is subject to o ur [general conditions](https://www.sib.swiss/training/terms-and-condition s).\n\nSIB abides by the [ELIXIR Code of Conduct](https://elixir-europe.or g/events/code-of-conduct). Participants of SIB courses are also required t o abide by the same code.\n\nFor more information\, please contact [traini ng@sib.swiss](mailto://training@sib.swiss). SUMMARY:Advanced Statistics: Statistical Modelling URL;VALUE=URI:https://www.sib.swiss/training/course/20250901_ASSM END:VEVENT END:VCALENDAR