Statistical Methods for Life Sciences

This national course 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.

Course content

Probability, sampling, and distributions for biological data
Sampling variability and resampling
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 using sampling, 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

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.
While we focus on biostatistics and machine learning, not coding, some coding is needed and the examples used are in R programming language. See below for entry requriements.

Entry requirements

BYOL (bring your own laptop) with R and R Studio installed.
Basic R programming skills
- using R as calculator
- being able to work with vectors and matrices, incl. subsetting and matrices 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 Quatro format

No prior biostatistical knowledge is assumed.

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.
We prioritise academic participants (students, staff, affiliated researchers) in Sweden. We welcome participants from industry and/or outside Sweden if there are seats available and the requirements criteria are met.

Fees

3000 SEK for academic participants
15 000 SEK for non-academic participants
the fee includes lunches and coffee

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.