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DTSTAMP:20260527T085053Z
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DTSTART:20260907T090000Z
DTEND:20260910T170000Z
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\n\nRegistration fees are **400
  CHF** for academics and **2000 CHF** for for-profit companies. \n\nWhile 
 participants are registered on a first come\, first served basis\, excepti
 ons may be made to ensure diversity and equity\, which may increase the ti
 me before your registration is confirmed. \n\nApplications will close on *
 *23/08/2026** or as soon as the places will be filled up. Deadline for fre
 e-of-charge cancellation is set to **23/08/2026**. Cancellation after this
  date will not be reimbursed. \n\nYou will be informed by email of your re
 gistration confirmation. Upon reception of the confirmation email\, partic
 ipants will be asked to confirm attendance by paying the fees within 5 day
 s. \n\n# Venue and Time\nThis course will be held at the University of Lau
 sanne.\n\nThe course will start at 9:00 and end around 17:00. \n\nPrecise 
 information will be provided to the participants in due time.\n\n\n#  Addi
 tional information\nCoordination: Diana Marek\, SIB Training group.\n\nWe 
 will recommend 1 ECTS credits for this course (given a passed exam at the 
 end of the course).\n\nYou are welcome to register to the SIB courses mail
 ing list to be informed of all future courses and workshops\, as well as a
 ll important deadlines using the form [here](https://lists.sib.swiss/posto
 rius/lists/courses.lists.sib.swiss/). \n\nPlease note that participation i
 n SIB courses is subject to our [general conditions](https://www.sib.swiss
 /training/terms-and-conditions). \n\nSIB abides by the [ELIXIR Code of Con
 duct](https://elixir-europe.org/events/code-of-conduct). Participants of S
 IB courses are also required to abide by the same code. \n\nFor more infor
 mation\, please contact [training@sib.swiss](mailto://training@sib.swiss).
SUMMARY:Advanced Statistics: Statistical Modelling
URL;VALUE=URI:https://www.sib.swiss/training/course/20260907_ADDMG
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