BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20260619T175856Z
UID:5fb52f0f-4248-49ed-b29a-6f659d6a87cf
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 designed for PhD students\, postdoctoral and other resear
 chers in the life sciences from both academia and industry who already use
  the R programming language and have some basic knowledge of statistics (i
 ncluding statistical tests\, correlation\, and linear models).\n\n# Learni
 ng outcomes\nAt the end of the course\, the participants should be able to
 :\n*  identify the appropriate model to analyze a dataset\n*  fit the chos
 en model using R\n*  assess the fit of the model\, as well as its limitati
 ons\n*  perform regularization or cross-validation\n\n### ***Knowledge / c
 ompetencies***\nThe course is intended for people already **familiar with 
 basic statistics and R**. Participants must be comfortable with topics suc
 h as hypothesis testing\, correlation and linear models\, and must have a 
 **prior knowledge of the "R" language and environment for statistical comp
 uting and graphics**. Participants who have already followed the SIB cours
 e ["Introduction to statistics with R"](https://www.sib.swiss/training/cou
 rse/STATR) or an equivalent course\, and have used its content in practice
  should fit this prerequisite.  \n\n**Before applying to this course\, ple
 ase self-assess your knowledge in stats and R to make sure this course is 
 right for you. Here are 2 quizzes:**  \n- [Quiz: Introduction to Statistic
 s	](https://gohighbrow.com/quiz-introduction-to-statistics/)\n	\n- ["Intro
 duction to R" self-assessment for the advanced statistics course](https://
 docs.google.com/forms/d/e/1FAIpQLSfXCnmLha0Ks4ZZZ42G_5MyIbGi-JhPayuHZ_P2jd
 XZEtXdqg/viewform)\n	\n\n\n### ***Technical***\nYou will need access to **
 a computer\, with at least 4 Gb of RAM\, as well as [R v. 4.5.0](https://c
 ran.r-project.org/) and [RStudio 2025.05.1-513](https://www.rstudio.com/pr
 oducts/rstudio/download/#download) software installed**. More information 
 about the packages needed will be provided in due time. \n\n# Brief course
  programme\n*  Monday: simple and multiple linear regression (theory\, dia
 gnostics\, and model selection)\n*  Tuesday: generalized linear models (bi
 nary data\, proportions\, and counts)\n*  Wednesday: mixed-effects linear 
 models\, longitudinal data analysis\n*  Thursday: Model Performance\, Sens
 itivity-Specificity ROC\, Regularization\, k-fold Cross validation and Le
 ave-one-out method (L1O)\n\n# Application\n\nRegistration fees are **400 C
 HF** for academics and **2000 CHF** for for-profit companies. \n\nWhile pa
 rticipants are registered on a first come\, first served basis\, exception
 s may be made to ensure diversity and equity\, which may increase the time
  before your registration is confirmed. \n\nApplications will close on **2
 3/08/2026** or as soon as the places will be filled up. Cancellation after
  **23/08/2026** will not be reimbursed. Please note that participation in 
 SIB courses is subject to our [general conditions](https://www.sib.swiss/l
 egal-documents).\n\nYou will be informed by email of your registration con
 firmation. Upon reception of the confirmation email\, participants will be
  asked to confirm attendance by paying the fees within **5 working days**.
 \n\n\n# Venue and Time\nThis course will be held at the [University of Lau
 sanne](https://planete.unil.ch/plan/) (Metro M1 line).\n\nThe course will 
 start at 9:00 and end around 17:00. \n\nPrecise information will be provid
 ed to the participants in due time.\n\n\n#  Additional information\nCoordi
 nation: Diana Marek\, SIB Training group.\n\nA **Certificate of Attendance
 ** will be sent provided you were present at the course\, whereas a **Cert
 ificate of Achievement** recommending [X] ECTS will be sent provided you p
 assed the exam.\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 i
 mportant deadlines using the form [here](https://lists.sib.swiss/mailman/l
 istinfo/courses).\n\nSIB abides by the [ELIXIR Code of Conduct](https://el
 ixir-europe.org/events/code-of-conduct). Participants of SIB courses are a
 lso required to abide by the same code.\n\nFor more information\, please c
 ontact [training@sib.swiss](mailto://training@sib.swiss).
SUMMARY:Advanced Statistics: Statistical Modelling
URL;VALUE=URI:https://www.sib.swiss/training/course/20260907_ADDMG
END:VEVENT
END:VCALENDAR