Statistical Methods in Life Sciences

 

 Statistical Methods in Life Sciences

This national course is open to PhD students, postdocs, and staff in the life sciences who need biostatistical skills in their research. Using real research examples, the course introduces core ideas of statistical inference and guides participants through widely used models such as regression, mixed-effects models for repeated or hierarchical data, and survival analysis. The course is suitable both for those with limited prior training and for researchers already applying statistical methods who want a stronger conceptual foundation to confidently choose, apply, and interpret statistical analyses in their own work.

 

Next course

• March 9th - 13th, 2026
• Trippelrummet (E10:1307-9), Navet, BMC, Husargatan 3, 751 23 Uppsala

 

 Application & Registration of interest

Application is now open. Apply here by January 30th, 2026.

 

 Important dates

• Application deadline: January 30th, 2026
• Confirmation to accepted students: February 6th, 2026
• Course days: March 9th - 13th, 2026

 

 Course content

• Probability, sampling, and distributions for biological data
• Sampling variability and resampling
• Multivariate data analysis: PCA and clustering
• Confidence intervals and hypothesis testing
• Linear models for continuous outcomes 
• Generalized linear models for binary and count data
• Mixed-effects models for repeated or hierarchical data,
• Survival analysis and time-to-event data

 

Learning outcomes

• Explain biological and technical sources of variability using probability concepts
• Quantify uncertainty due to sampling using resampling and appropriate distributions
• Identify structure in multivariate data using PCA and clustering
• Interpret confidence intervals and hypothesis tests correctly
• Build and interpret linear models for continuous biological outcomes
• Apply generalized linear models to binary and count data
• Analyze repeated or hierarchical data using mixed-effects models
• Analyze time-to-event data using survival analysis

 

 Schedule

Preliminary course schedule can be found here

 

 Education

In this course we focus on an active learning approach. The course participants are expected to do some pre-course reading and exercises, corresponding up to 40h studying. The education consists of teaching blocks alternating between lectures, exercises, group discussions, live coding sessions etc.

 

Entry requirements

• Basic R programming skills (check your skills by taking our self-assessment test)
  • • using R as calculator
    • being able to work with vectors and matrices, incl. subsetting and matrix multiplication 
    • reading in data from .csv files, e.g. with read_csv()
    • printing top few rows or last few rows, e.g. with head() and tail()
    • using in-built summary functions such as sum(), min() or max()
    • being able to use documentation pages for R functions, e.g. with help() or ?()
    • using if else statements, writing simple loops and functions.
    • making simple plots (scatter plots, histograms), both with plot() and ggplot()
    • using tidyverse() for data transformations, e.g. filtering rows, selecting columns, creating new columns etc. 
    • being able to install CRAN packages e.g. with install.packages()
    • being familiar with R Markdown or Quarto format
• No prior biostatistical knowledge is assumed, only basic math skills (pre-course studying materials will be available upon course acceptance). 
• BYOL (bring your own laptop) with R and R Studio installed

 

 Selection criteria

• Due to limited space the course can accommodate maximum of 24 participants. If we receive more applications, participants will be selected based on several criteria. Selection criteria include correct entry requirements, motivation to attend the course as well as gender and geographical balance.
• NBIS prioritises academic participants (students, staff, affiliated researchers) in Sweden. We can accept participants from industry and/or outside Sweden if we have seats available and the requirements criteria are met.

 

 Fees

3000 SEK for academic participants

15 000 SEK for non-academic participants

includes lunches and coffee 

 

 Travel info

For travel information and hotel bookings see Travel Information page 

 

 Course credits

• Upon successful course completion, assessed based on active participation in all course session, we will issue a course certificate.

• Please note that we are not able to provide any formal university credits (högskolepoäng). Many universities, however, recognize the attendance in our courses, and award 1.5 HPs, corresponding to 40h of studying. It is up to participants to clarify and arrange credit transfer with the relevant university department.

 

 Teaching team

• Olga Dethlefsen «olga.dethlefsen@nbis.se»
• Eva Freyhult «eva.freyhult@nbis.se»
• Payam Emami «payam.emami@nbis.se»
• Julie Lorent «julie.lorent@nbis.se»
• Miguel Redondo miguel.angel.redondo@nbis.se

 

 Contact us

• edu.ml-biostats@nbis.se
• olga.dethlefsen@nbis.se
• eva.freyhult@nbis.se