Statistical Methods in Life Sciences

Description

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.

This course is similar to our previous course Introduction to Biostatistics and Machine Learning course, but with a stronger focus on statistical methods.

Topics covered

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

A course fee of 3000 SEK for academic participants and 15000 SEK for non-academic participants will be invoiced to accepted participants. The fee includes lunches, coffee and snacks. Please note that NBIS cannot invoice individuals

Learning outcomes

By the end of this course, participants will be able to:

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

Pre-requisites

Basic R programming skills. You can check your skills by taking our self-assessment test.

Applicants are expected to bring their own laptops. A reasonably modern laptop with linux/unix, mac or windows OS and internet connection. Latest version of R and R Studio should be installed.