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DTSTAMP:20260715T125620Z
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DTSTART:20260907T090000Z
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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 chosen model using R\n*  **Assess** the fit of the model\, as well as 
 its limitations\n*  **Perform** regularization or cross-validation\n\n## P
 rerequisites \n### Knowledge / competencies \nThe course is intended for p
 eople already **familiar with basic statistics and R**. Participants must 
 be comfortable with topics such as hypothesis testing\, correlation and li
 near models\, and must have a **prior knowledge of the "R" language and en
 vironment for statistical computing and graphics**. Participants who have 
 already followed the SIB course ["Introduction to statistics with R"](http
 s://www.sib.swiss/training/course/STATR) or an equivalent course\, and hav
 e used its content in practice should 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 right for you. Here are 2 quizzes:**  \n- [Q
 uiz: Introduction to Statistics	](https://gohighbrow.com/quiz-introduction
 -to-statistics/)\n	\n- ["Introduction to R" self-assessment for the advanc
 ed statistics course](https://docs.google.com/forms/d/e/1FAIpQLSfXCnmLha0K
 s4ZZZ42G_5MyIbGi-JhPayuHZ_P2jdXZEtXdqg/viewform)\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://cran.r-project.org/) and [RStudio 2025.05.1-513](htt
 ps://www.rstudio.com/products/rstudio/download/#download) software install
 ed**. More information about the packages needed will be provided in due t
 ime. \n\n# Brief course programme\n*  Monday: simple and multiple linear r
 egression (theory\, diagnostics\, and model selection)\n*  Tuesday: genera
 lized linear models (binary data\, proportions\, and counts)\n*  Wednesday
 : mixed-effects linear models\, longitudinal data analysis\n*  Thursday: M
 odel Performance\, Sensitivity-Specificity ROC\, Regularization\, k-fold C
 ross validation and Leave-one-out method (L1O)\n\n# Application\n\nRegist
 ration fees are **400 CHF** for academics and **2000 CHF** for for-profit 
 companies. \n\nWhile participants are registered on a first come\, first s
 erved basis\, exceptions may be made to ensure diversity and equity\, whic
 h may increase the time before your registration is confirmed. \n\nApplica
 tions will close on **23/08/2026** 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/legal-documents).\n\nYou will be informed by email o
 f your registration confirmation. Upon reception of the confirmation email
 \, participants will be asked to confirm attendance by paying the fees wit
 hin **5 working days**.\n\n\n# Venue and Time\nThis course will be held at
  the [University of Lausanne](https://planete.unil.ch/plan/) (Metro M1 lin
 e).\n\nThe course will start at 9:00 and end around 17:00. \n\nPrecise inf
 ormation will be provided to the participants in due time.\n\n\n#  Additio
 nal information\nCoordination: Diana Marek\, SIB Training group.\n\nA **Ce
 rtificate of Attendance** will be sent provided you were present at the co
 urse\, whereas a **Certificate of Achievement** recommending 1 ECTS will b
 e sent provided you passed the exam.\n\nYou are welcome to register to the
  SIB courses mailing list to be informed of all future courses and worksho
 ps\, as well as all important deadlines using the form [here](https://list
 s.sib.swiss/mailman/listinfo/courses).\n\nSIB abides by the [ELIXIR Code o
 f Conduct](https://elixir-europe.org/events/code-of-conduct). Participants
  of SIB courses are also required to abide by the same code.\n\nFor more i
 nformation\, please contact [training@sib.swiss](mailto://training@sib.swi
 ss).
SUMMARY:Advanced Statistics: Statistical Modelling
URL;VALUE=URI:https://www.sib.swiss/training/course/20260907_ADDMG
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